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Memetic Evolution of Training Sets with Adaptive Radial Basis Kernels for Support Vector Machines

Jakub Nalepa, Wojciech Dudzik, Michal Kawulok

Auto-TLDR; Memetic Algorithm for Evolving Support Vector Machines with Adaptive Kernels

Abstract Slides Poster

Support vector machines (SVMs) are a supervised learning technique that can be applied in both binary and multi-class classification and regression tasks. SVMs seamlessly handle continuous and categorical variables. Their training is, however, both time- and memory-costly for large training data, and selecting an incorrect kernel function or its hyperparameters leads to suboptimal decision hyperplanes. In this paper, we introduce a memetic algorithm for evolving SVM training sets with adaptive radial basis function kernels to not only make the deployment of SVMs easier for emerging big data applications, but also to improve their generalization abilities over the unseen data. We build upon two observations: first, only a small subset of all training vectors, called the support vectors, contribute to the position of the decision boundary, hence the other vectors can be removed from the training set without deteriorating the performance of the model. Second, selecting different kernel hyperparameters for different training vectors may help better reflect the subtle characteristics of the space while determining the hyperplane. The experiments over almost 100 benchmark and synthetic sets showed that our algorithm delivers models outperforming both SVMs optimized using state-of-the-art evolutionary techniques, and other supervised learners.

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Categorizing the Feature Space for Two-Class Imbalance Learning

Rosa Sicilia, Ermanno Cordelli, Paolo Soda

Auto-TLDR; Efficient Ensemble of Classifiers for Minority Class Inference

Abstract Slides Poster Similar

Class imbalance limits the performance of most learning algorithms, resulting in a low recognition rate for samples belonging to the minority class. Although there are different strategies to address this problem, methods that generate ensemble of classifiers have proven to be effective in several applications. This paper presents a new strategy to construct the training set of each classifier in the ensemble by exploiting information in the feature space that can give rise to unreliable classifications, which are determined by a novel algorithm here introduced. The performance of our proposal is compared against multiple standard ensemble approaches on 25 publicly available datasets, showing promising results.

Mean Decision Rules Method with Smart Sampling for Fast Large-Scale Binary SVM Classification

Alexandra Makarova, Mikhail Kurbakov, Valentina Sulimova

Auto-TLDR; Improving Mean Decision Rule for Large-Scale Binary SVM Problems

Abstract Slides Poster Similar

This paper relies on the Mean Decision Rule (MDR) method for solving large-scale binary SVM problems. It consists in taking small random samples of the full dataset and separate training for each of them with consecutive averaging the respective individual decision rules to obtain a final one. This paper proposes two new approaches to improve it. The first proposed approach is a new sampling technique that exploits SVM and MDR properties to fast form so called smart samples by selecting only the objects, that are candidates to be the support ones. The proposed technique essentially increases MDR convergence and allows to reach the highest quality in less time. In the case of kernel-based MDR (KMDR) the proposed sampling technique allows additionally to reduce the number of support objects in the final decision rule and, as a result, to decrease the recognition time. The second proposed approach is a new data strategy to accelerate random access to large datasets stored in the traditional libsvm format. The proposed strategy allows to quickly extract random subsets of objects from a file and load them into RAM, and is it also suitable for any sampling-based methods, including stochastic gradient methods. Joint using of the proposed approaches with (K)MDR allows to obtain the best (or near the best) decision of large-scale binary SVM problems faster, compared to the existing SVM solvers.

Position-Aware Safe Boundary Interpolation Oversampling

Yongxu Liu, Yan Liu

Auto-TLDR; PABIO: Position-Aware Safe Boundary Interpolation-Based Oversampling for Imbalanced Data

Abstract Slides Poster Similar

The class imbalance problem is characterized by the unequal distribution of different class samples, usually resulting in a learning bias toward the majority class. In the past decades, kinds of techniques have been proposed to alleviate this problem. Among those approaches, one promising method, interpolation- based oversampling, proposes to generate synthetic minority samples based on selected reference data, which can effectively solve the skewed distribution of data samples. However, there are several unsolved issues in interpolation-based oversampling. Existing methods often suffer from noisy synthetic samples due to improper data clusterings and unsatisfactory reference selection. In this paper, we propose the position-aware safe boundary interpolation oversampling algorithm (PABIO) to address such issues. We firstly introduce a combined clustering algorithm for minority samples to overcome the shortage of clustering using only distance-based or density-based. Then a position- aware interpolation-based oversampling algorithm is proposed for different minority clusters. Especially, we develop a novel method to leverage the majority class information to learn a safe boundary for generating synthetic points. The proposed PABIO is evaluated on multiple imbalanced data sets classified by two base classifiers: support vector machine (SVM) and C4.5 decision tree classifier. Experimental results show that our proposed PABIO outperforms other baselines among benchmark data sets.

A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios

Ayush Tripathi, Rupayan Chakraborty, Sunil Kumar Kopparapu

Auto-TLDR; Synthetic Minority OverSampling Technique for Imbalanced Data

Abstract Slides Poster Similar

Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. This is primarily due to the tendency of the classifier to be biased towards the majority classes in the imbalanced dataset. In this paper, we propose a novel three step technique to address imbalanced data. As a first step we significantly oversample the minority class distribution by employing the traditional Synthetic Minority OverSampling Technique (SMOTE) algorithm using the neighborhood of the minority class samples and in the next step we partition the generated samples using a Gaussian-Mixture Model based clustering algorithm. In the final step synthetic data samples are chosen based on the weight associated with the cluster, the weight itself being determined by the distribution of the majority class samples. Extensive experiments on several standard datasets from diverse domains show the usefulness of the proposed technique in comparison with the original SMOTE and its state-of-the-art variants algorithms.

Multi-Attribute Learning with Highly Imbalanced Data

Lady Viviana Beltran Beltran, Mickaël Coustaty, Nicholas Journet, Juan C. Caicedo, Antoine Doucet

Auto-TLDR; Data Imbalance in Multi-Attribute Deep Learning Models: Adaptation to face each one of the problems derived from imbalance

Abstract Slides Poster Similar

Data is one of the most important keys for success when studying a simple or a complex phenomenon. With the use of deep-learning exploding and its democratization, non-computer science experts may struggle to use highly complex deep learning architectures, even when straightforward models offer them suitable performances. In this article, we study the specific and common problem of data imbalance in real databases as most of the bad performance problems are due to the data itself. We review two points: first, when the data contains different levels of imbalance. Classical imbalanced learning strategies cannot be directly applied when using multi-attribute deep learning models, i.e., multi-task and multi-label architectures. Therefore, one of our contributions is our proposed adaptations to face each one of the problems derived from imbalance. Second, we demonstrate that with little to no imbalance, straightforward deep learning models work well. However, for non-experts, these models can be seen as black boxes, where all the effort is put in pre-processing the data. To simplify the problem, we performed the classification task ignoring information that is costly to extract, such as part localization which is widely used in the state of the art of attribute classification. We make use of a widely known attribute database, CUB-200-2011 - CUB as our main use case due to its deeply imbalanced nature, along with two better structured databases: celebA and Awa2. All of them contain multi-attribute annotations. The results of highly fine-grained attribute learning over CUB demonstrate that in the presence of imbalance, by using our proposed strategies is possible to have competitive results against the state of the art, while taking advantage of multi-attribute deep learning models. We also report results for two better-structured databases over which our models over-perform the state of the art.

Classifier Pool Generation Based on a Two-Level Diversity Approach

Marcos Monteiro, Alceu Britto, Jean Paul Barddal, Luiz Oliveira, Robert Sabourin

Auto-TLDR; Diversity-Based Pool Generation with Dynamic Classifier Selection and Dynamic Ensemble Selection

Abstract Slides Poster Similar

This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions. First, the behavior of complexity measures is assessed by considering several subsamples of the dataset. The complexity measures with high variability across the subsamples are selected for posterior pool adaptation, where an evolutionary algorithm optimizes diversity in both complexity and decision spaces. A robust experimental protocol with 28 datasets and 20 replications is used to evaluate the proposed method. Results show significant accuracy improvements in 69.4\% of the experiments when Dynamic Classifier Selection and Dynamic Ensemble Selection methods are applied.

Creating Classifier Ensembles through Meta-Heuristic Algorithms for Aerial Scene Classification

Álvaro Roberto Ferreira Jr., Gustavo Gustavo Henrique De Rosa, Joao Paulo Papa, Gustavo Carneiro, Fabio Augusto Faria

Auto-TLDR; Univariate Marginal Distribution Algorithm for Aerial Scene Classification Using Meta-Heuristic Optimization

Abstract Slides Poster Similar

Aerial scene classification is a challenging task to be solved in the remote sensing area, whereas deep learning approaches, such as Convolutional Neural Networks (CNN), are being widely employed to overcome such a problem. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the nurturing of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized-ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Finally, one can observe that the Univariate Marginal Distribution Algorithm (UMDA) overcame popular literature meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization considering the adopted criteria in the performed experiments.

Minority Class Oriented Active Learning for Imbalanced Datasets

Umang Aggarwal, Adrian Popescu, Celine Hudelot

Auto-TLDR; Active Learning for Imbalanced Datasets

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Active learning aims to optimize the dataset annotation process when resources are constrained. Most existing methods are designed for balanced datasets. Their practical applicability is limited by the fact that a majority of real-life datasets are actually imbalanced. Here, we introduce a new active learning method which is designed for imbalanced datasets. It favors samples likely to be in minority classes so as to reduce the imbalance of the labeled subset and create a better representation for these classes. We also compare two training schemes for active learning: (1) the one commonly deployed in deep active learning using model fine tuning for each iteration and (2) a scheme which is inspired by transfer learning and exploits generic pre-trained models and train shallow classifiers for each iteration. Evaluation is run with three imbalanced datasets. Results show that the proposed active learning method outperforms competitive baselines. Equally interesting, they also indicate that the transfer learning training scheme outperforms model fine tuning if features are transferable from the generic dataset to the unlabeled one. This last result is surprising and should encourage the community to explore the design of deep active learning methods.

Using Machine Learning to Refer Patients with Chronic Kidney Disease to Secondary Care

Lee Au-Yeung, Xianghua Xie, Timothy Marcus Scale, James Anthony Chess

Auto-TLDR; A Machine Learning Approach for Chronic Kidney Disease Prediction using Blood Test Data

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There has been growing interest recently in using machine learning techniques as an aid in clinical medicine. Machine learning offers a range of classification algorithms which can be applied to medical data to aid in making clinical predictions. Recent studies have demonstrated the high predictive accuracy of various classification algorithms applied to clinical data. Several studies have already been conducted in diagnosing or predicting chronic kidney disease at various stages using different sets of variables. In this study we are investigating the use machine learning techniques with blood test data. Such a system could aid renal teams in making recommendations to primary care general practitioners to refer patients to secondary care where patients may benefit from earlier specialist assessment and medical intervention. We are able to achieve an overall accuracy of 88.48\% using logistic regression, 87.12\% using ANN and 85.29\% using SVM. ANNs performed with the highest sensitivity at 89.74\% compared to 86.67\% for logistic regression and 85.51\% for SVM.

Decision Snippet Features

Pascal Welke, Fouad Alkhoury, Christian Bauckhage, Stefan Wrobel

Auto-TLDR; Decision Snippet Features for Interpretability

Abstract Slides Poster Similar

Decision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees -- random forests -- are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in low-memory embedded devices. We introduce \emph{Decision Snippet Features}, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude.

The eXPose Approach to Crosslier Detection

Antonio Barata, Frank Takes, Hendrik Van Den Herik, Cor Veenman

Auto-TLDR; EXPose: Crosslier Detection Based on Supervised Category Modeling

Abstract Slides Poster Similar

Transit of wasteful materials within the European Union is highly regulated through a system of permits. Waste processing costs vary greatly depending on the waste category of a permit. Therefore, companies may have a financial incentive to allege transporting waste with erroneous categorisation. Our goal is to assist inspectors in selecting potentially manipulated permits for further investigation, making their task more effective and efficient. Due to data limitations, a supervised learning approach based on historical cases is not possible. Standard unsupervised approaches, such as outlier detection and data quality-assurance techniques, are not suited since we are interested in targeting non-random modifications in both category and category-correlated features. For this purpose we (1) introduce the concept of crosslier: an anomalous instance of a category which lies across other categories; (2) propose eXPose: a novel approach to crosslier detection based on supervised category modelling; and (3) present the crosslier diagram: a visualisation tool specifically designed for domain experts to easily assess crossliers. We compare eXPose against traditional outlier detection methods in various benchmark datasets with synthetic crossliers and show the superior performance of our method in targeting these instances.

Adaptive Matching of Kernel Means

Miao Cheng, Xinge You

Auto-TLDR; Adaptive Matching of Kernel Means for Knowledge Discovery and Feature Learning

Abstract Slides Poster Similar

As a promising step, the performance of data analysis and feature learning are able to be improved if certain pattern matching mechanism is available. One of the feasible solutions can refer to the importance estimation of instances, and consequently, kernel mean matching (KMM) has become an important method for knowledge discovery and novelty detection in general. Furthermore, the existing KMM methods have focused on concrete learning frameworks. In this work, a novel approach to adaptive matching of kernel means is proposed, and selected data with high importance are adopted to achieve calculation efficiency with optimization. In addition, scalable learning can be conducted in proposed method as a generalized solution with appended data. The experimental results on a wide variety of real-world data sets demonstrate the proposed method is able to give outstanding performance compared with several state-of-the-art methods, while calculation efficiency can be preserved.

Automatic Classification of Human Granulosa Cells in Assisted Reproductive Technology Using Vibrational Spectroscopy Imaging

Marina Paolanti, Emanuele Frontoni, Giorgia Gioacchini, Giorgini Elisabetta, Notarstefano Valentina, Zacà Carlotta, Carnevali Oliana, Andrea Borini, Marco Mameli

Auto-TLDR; Predicting Oocyte Quality in Assisted Reproductive Technology Using Machine Learning Techniques

Abstract Slides Poster Similar

In the field of reproductive technology, the biochemical composition of female gametes has been successfully investigated with the use of vibrational spectroscopy. Currently, in assistive reproductive technology (ART), there are no shared criteria for the choice of oocyte, and automatic classification methods for the best quality oocytes have not yet been applied. In this paper, considering the lack of criteria in Assisted Reproductive Technology (ART), we use Machine Learning (ML) techniques to predict oocyte quality for a successful pregnancy. To improve the chances of successful implantation and minimize any complications during the pregnancy, Fourier transform infrared microspectroscopy (FTIRM) analysis has been applied on granulosa cells (GCs) collected along with the oocytes during oocyte aspiration, as it is routinely done in ART, and specific spectral biomarkers were selected by multivariate statistical analysis. A proprietary biological reference dataset (BRD) was successfully collected to predict the best oocyte for a successful pregnancy. Personal health information are stored, maintained and backed up using a cloud computing service. Using a user-friendly interface, the user will evaluate whether or not the selected oocyte will have a positive result. This interface includes a dashboard for retrospective analysis, reporting, real-time processing, and statistical analysis. The experimental results are promising and confirm the efficiency of the method in terms of classification metrics: precision, recall, and F1-score (F1) measures.

Detecting Marine Species in Echograms Via Traditional, Hybrid, and Deep Learning Frameworks

Porto Marques Tunai, Alireza Rezvanifar, Melissa Cote, Alexandra Branzan Albu, Kaan Ersahin, Todd Mudge, Stephane Gauthier

Auto-TLDR; End-to-End Deep Learning for Echogram Interpretation of Marine Species in Echograms

Abstract Slides Poster Similar

This paper provides a comprehensive comparative study of traditional, hybrid, and deep learning (DL) methods for detecting marine species in echograms. Acoustic backscatter data obtained from multi-frequency echosounders is visualized as echograms and typically interpreted by marine biologists via manual or semi-automatic methods, which are time-consuming. Challenges related to automatic echogram interpretation are the variable size and acoustic properties of the biological targets (marine life), along with significant inter-class similarities. Our study explores and compares three types of approaches that cover the entire range of machine learning methods. Based on our experimental results, we conclude that an end-to-end DL-based framework, that can be readily scaled to accommodate new species, is overall preferable to other learning approaches for echogram interpretation, even when only a limited number of annotated training samples is available.

Boundary Bagging to Address Training Data Issues in Ensemble Classification

Samia Boukir, Wei Feng

Auto-TLDR; Bagging Ensemble Learning for Multi-Class Imbalanced Classification

Abstract Poster Similar

The characteristics of training data is a fundamental consideration when constructing any supervised classifier. Class mislabelling and imbalance are major training data issues that often adversely affect machine learning algorithms, including ensembles. This work proposes extended bagging algorithms to better handle noisy and multi-class imbalanced classification tasks. These algorithms upgrade the sampling procedure by taking benefit of the confidence in ensemble classification outcome. The underlying idea is that a bagging ensemble learning algorithm can achieve greater performance if it is allowed to choose the data from which it learns. The effectiveness of the proposed methods is demonstrated in performing classification on 10 various data sets.

Multi-annotator Probabilistic Active Learning

Marek Herde, Daniel Kottke, Denis Huseljic, Bernhard Sick

Auto-TLDR; MaPAL: Multi-annotator Probabilistic Active Learning

Abstract Slides Poster Similar

Classifiers require annotations of instances, i.e., class labels, for training. An annotation process is often costly due to its manual execution through human annotators. Active learning (AL) aims at reducing the annotation costs by selecting instances from which the classifier is expected to learn the most. Many AL strategies assume the availability of a single omniscient annotator. In this article, we overcome this limitation by considering multiple error-prone annotators. We propose a novel AL strategy multi-annotator probabilistic active learning (MaPAL). Due to the nature of learning with error-prone annotators, it must not only select instances but annotators, too. MaPAL builds on a decision-theoretic framework and selects instance-annotator pairs maximizing the classifier's expected performance. Experiments on a variety of data sets demonstrate MaPAL's superior performance compared to five related AL strategies.

Deep Learning Based Sepsis Intervention: The Modelling and Prediction of Severe Sepsis Onset

Gavin Tsang, Xianghua Xie

Auto-TLDR; Predicting Sepsis onset by up to six hours prior using a boosted cascading training methodology and adjustable margin hinge loss function

Abstract Slides Poster Similar

Sepsis presents a significant challenge to healthcare providers during critical care scenarios such as within an intensive care unit. The prognosis of the onset of severe septic shock results in significant increases in mortality rate, length of stay and readmission rates. Continual advancements in health informatics data allows for applications within the machine learning field to predict sepsis onset in a timely manner, allowing for effective preventative intervention of severe septic shock. A novel deep learning application is proposed to provide effective prediction of sepsis onset by up to six hours prior, involving the use of novel concepts such as a boosted cascading training methodology and adjustable margin hinge loss function. The proposed methodology provides statistically significant improvements to that of current machine learning based modelling applications based off the Physionet Computing in Cardiology 2019 challenge. Results show test F1 scores of 0.420, a significant improvement of 0.281 as compared to the next best challenger results.

A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Pierluigi Carcagni, Marco Leo, Andrea Cuna, Giuseppe Celeste, Cosimo Distante

Auto-TLDR; RegNet: Deep Investigation of Convolutional Neural Networks for Automatic Classification of Skin Lesions

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Computer vision-based techniques are more and more employed in healthcare and medical fields nowadays in order, principally, to be as a support to the experienced medical staff to help them to make a quick and correct diagnosis. One of the hot topics in this arena concerns the automatic classification of skin lesions. Several promising works exist about it, mainly leveraging Convolutional Neural Networks (CNN), but proposed pipeline mainly rely on complex data preprocessing and there is no systematic investigation about how available deep models can actually reach the accuracy needed for real applications. In order to overcome these drawbacks, in this work, an end-to-end pipeline is introduced and some of the most recent Convolutional Neural Networks (CNNs) architectures are included in it and compared on the largest common benchmark dataset recently introduced. To this aim, for the first time in this application context, a new network design paradigm, namely RegNet, has been exploited to get the best models among a population of configurations. The paper introduces a threefold level of contribution and novelty with respect the previous literature: the deep investigation of several CNN architectures driving to a consistent improvement of the lesions recognition accuracy, the exploitation of a new network design paradigm able to study the behavior of populations of models and a deep discussion about pro and cons of each analyzed method paving the path towards new research lines.

Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes

Andre Mendes, Julian Togelius, Leandro Dos Santos Coelho

Auto-TLDR; Multi-Task Learning and Semi-Supervised Learning for Multi-Stage Processes

Abstract Similar

In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers in this scenario is challenging since information in the early stages may not contain distinct patterns to learn (underfitting). In contrast, the small sample size in later stages can cause overfitting. We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multi-task learning (MTL), and multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE with an MTL component so it can jointly reconstruct the original input and use feature nets to predict the features for the next stages. We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions. Using real-world data from different domains (selection process, medical diagnosis), we show that our approach outperforms other state-of-the-art methods.

Hierarchical Routing Mixture of Experts

Wenbo Zhao, Yang Gao, Shahan Ali Memon, Bhiksha Raj, Rita Singh

Auto-TLDR; A Binary Tree-structured Hierarchical Routing Mixture of Experts for Regression

Abstract Slides Poster Similar

In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model, and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate the effectiveness of our method compared to a variety of other regression models.

Algorithm Recommendation for Data Streams

Jáder Martins Camboim De Sá, Andre Luis Debiaso Rossi, Gustavo Enrique De Almeida Prado Alves Batista, Luís Paulo Faina Garcia

Auto-TLDR; Meta-Learning for Algorithm Selection in Time-Changing Data Streams

Abstract Slides Poster Similar

In the last decades, many companies are taking advantage of massive data generation at high frequencies through knowledge discovery to identify valuable information. Machine learning techniques can be employed for knowledge discovery, since they are able to extract patterns from data and induce models to predict future events. However, dynamic and evolving environments generate streams of data that usually are non-stationary. Models induced in these scenarios may perish over time due to seasonality or concept drift. The periodic retraining could help but the fixed algorithm's hypothesis space could no longer be appropriate. An alternative solution is to use meta-learning for periodic algorithm selection in time-changing environments, choosing the bias that best suits the current data. In this paper, we present an enhanced framework for data streams algorithm selection based on MetaStream. Our approach uses meta-learning and incremental learning to actively select the best algorithm for the current concept in a time-changing. Different from previous works, a set of cutting edge meta-features and an incremental learning approach in the meta-level based on LightGBM are used. The results show that this new strategy can improve the recommendation of the best algorithm more accurately in time-changing data.

Tensorized Feature Spaces for Feature Explosion

Ravdeep Pasricha, Pravallika Devineni, Evangelos Papalexakis, Ramakrishnan Kannan

Auto-TLDR; Tensor Rank Decomposition for Hyperspectral Image Classification

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In this paper, we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose ORION, which takes as input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in R^K to high dimensional space in R^R, where R >> K, say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three-dimensional tensor. We demonstrate the effectiveness of our method with experiments on three publicly available hyperspectral datasets with labeled pixels and compare their classification performance against traditional linear and non-linear supervised learning methods such as SVM with Linear, Polynomial, RBF kernels, and the Multi-Layer Perceptron model. Finally, we explore the relationship between the rank of the tensor decomposition and the classification accuracy using several hyperspectral datasets with ground truth.

A Cheaper Rectified-Nearest-Feature-Line-Segment Classifier Based on Safe Points

Mauricio Orozco-Alzate, Manuele Bicego

Auto-TLDR; Rectified Nearest Feature Line Segment Segment Classifier

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The Rectified Nearest Feature Line Segment (RN-FLS) classifier is an improved version of the Nearest Feature Line (NFL) classification rule. RNFLS corrects two drawbacks of NFL, namely the interpolation and extrapolation inaccuracies, by applying two consecutive processes - segmentation and rectification - to the initial set of feature lines. The main drawbacks of this technique, occurring in both training and test phases, are the high computational cost of the rectification procedure and the exponential explosion of the number of lines. We propose a cheaper version of RNFLS, based on a characterization of the points that should form good lines. The characterization relies on a recent neighborhood-based principle that categorizes objects into four types: safe, borderline, rare and outliers, depending on the position of each point with respect to the other classes. The proposed approach represents a variant of RNFLS in the sense that it only considers lines between safe points. This allows a drastic reduction in the computational burden imposed by RNFLS. We carried out an empirical and thorough analysis based on different public data sets, showing that our proposed approach, in general, is not significantly different from RNFLS, but cheaper since the consideration of likely irrelevant feature line segments is avoided.

An Effective Approach for Neural Network Training Based on Comprehensive Learning

Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin

Auto-TLDR; ClPSO-LM: A Hybrid Algorithm for Multi-layer Feed-Forward Neural Networks

Abstract Similar

Multi-layer feed-forward neural networks have been used to tackle many complex practical applications. Their performance is closely related to the success of training algorithms which adapt the weights in the network. Although conventional algorithms such as back-propagation are widely used, they suffer from drawbacks such as a tendency to get trapped in local optima. Stochastic optimisation algorithms, and in particular population-based metaheuristics, represent a useful alternative in this context. In this paper, we propose an effective hybrid algorithm, CLPSO-LM, which is based on particle swarm optimisation (PSO), a population-based metaheuristic algorithm, the Levenberg-Marquardt (LM) algorithm as a local search algorithm, and a comprehensive learning (CL) strategy. The CL strategy in our algorithm is responsible for improving the exploration ability of the algorithm and preventing premature convergence using neighbour candidate solutions in PSO. The best position found by comprehensive learning PSO is then used as the initial network weights for the LM algorithm. An extensive set of experiments on different benchmark datasets and comparison to various conventional and population-based algorithms shows very competitive performance of our CLPSO-LM algorithm.

A Comparison of Neural Network Approaches for Melanoma Classification

Maria Frasca, Michele Nappi, Michele Risi, Genoveffa Tortora, Alessia Auriemma Citarella

Auto-TLDR; Classification of Melanoma Using Deep Neural Network Methodologies

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Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist’s recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the pre- processing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.

Towards Tackling Multi-Label Imbalances in Remote Sensing Imagery

Dominik Koßmann, Thorsten Wilhelm, Gernot Fink

Auto-TLDR; Class imbalance in land cover datasets using attribute encoding schemes

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Recent advances in automated image analysis have lead to an increased number of proposed datasets in remote sensing applications. This permits the successful employment of data hungry state-of-the-art deep neural networks. However, the Earth is not covered equally by semantically meaningful classes. Thus, many land cover datasets suffer from a severe class imbalance. We show that by taking appropriate measures, the performance in the minority classes can be improved by up to 30 percent without affecting the performance in the majority classes strongly. Additionally, we investigate the use of an attribute encoding scheme to represent the inherent class hierarchies commonly observed in land cover analysis.

Budgeted Batch Mode Active Learning with Generalized Cost and Utility Functions

Arvind Agarwal, Shashank Mujumdar, Nitin Gupta, Sameep Mehta

Auto-TLDR; Active Learning Based on Utility and Cost Functions

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Active learning reduces the labeling cost by actively querying labels for the most valuable data points. Typical active learning methods select the most informative examples one-at-a-time, their batch variants exist where a set of most informative points are selected. These points are selected in such a way that when added to the training data along with their labels, they provide maximum benefit to the underlying model. In this paper, we present a learning framework that actively selects optimal set of examples (in a batch) within a given budget, based on given utility and cost functions. The framework is generic enough to incorporate any utility and any cost function defined on a set of examples. Furthermore, we propose a novel utility function based on the Facility Location problem that considers three important characteristics of utility i.e., diversity, density and point utility. We also propose a novel cost function, by formulating the cost computation problem as an optimization problem, the solution to which turns out to be the minimum spanning tree. Thus, our framework provides the optimal batch of points within the given budget based on the cost and utility functions. We evaluate our method on several data sets and show its superior performance over baseline methods.

An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification

Victor Souza, Adriano Oliveira, Rafael Menelau Oliveira E Cruz, Robert Sabourin

Auto-TLDR; Overfitting of SigNet using Binary Particle Swarm Optimization

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SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS-960 datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context.

Weakly Supervised Learning through Rank-Based Contextual Measures

João Gabriel Camacho Presotto, Lucas Pascotti Valem, Nikolas Gomes De Sá, Daniel Carlos Guimaraes Pedronette, Joao Paulo Papa

Auto-TLDR; Exploiting Unlabeled Data for Weakly Supervised Classification of Multimedia Data

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Machine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive growth, becoming ubiquitous in diverse domains. Due to the huge increase in multimedia data collections and the lack of labeled data in several scenarios, creating methods capable of exploiting the unlabeled data and operating under weakly supervision is imperative. In this work, we propose a rank-based model to exploit contextual information encoded in the unlabeled data in order to perform weakly supervised classification. We employ different rank-based correlation measures for identifying strong similarities relationships and expanding the labeled set in an unsupervised way. Subsequently, the extended labeled set is used by a classifier to achieve better accuracy results. The proposed weakly supervised approach was evaluated on multimedia classification tasks, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 4 public image datasets and different features. Very positive gains were achieved in comparison with various semi-supervised and supervised classifiers taken as baselines when considering the same amount of labeled data.

Proximity Isolation Forests

Antonella Mensi, Manuele Bicego, David Tax

Auto-TLDR; Proximity Isolation Forests for Non-vectorial Data

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Isolation Forests are a very successful approach for solving outlier detection tasks. Isolation Forests are based on classical Random Forest classifiers that require feature vectors as input. There are many situations where vectorial data is not readily available, for instance when dealing with input sequences or strings. In these situations, one can extract higher level characteristics from the input, which is typically hard and often loses valuable information. An alternative is to define a proximity between the input objects, which can be more intuitive. In this paper we propose the Proximity Isolation Forests that extend the Isolation Forests to non-vectorial data. The introduced methodology has been thoroughly evaluated on 8 different problems and it achieves very good results also when compared to other techniques.

A Novel Random Forest Dissimilarity Measure for Multi-View Learning

Hongliu Cao, Simon Bernard, Robert Sabourin, Laurent Heutte

Auto-TLDR; Multi-view Learning with Random Forest Relation Measure and Instance Hardness

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Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

MD-kNN: An Instance-Based Approach for Multi-Dimensional Classification

Bin-Bin Jia, Min-Ling Zhang

Auto-TLDR; MD-kNN: Adapting Instance-based Techniques for Multi-dimensional Classification

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Multi-dimensional classification (MDC) deals with the problem where each instance is associated with multiple class variables, each of which corresponds to a specific class space. One of the mainstream solutions for MDC is to adapt traditional machine learning techniques to deal with MDC data. In this paper, a first attempt towards adapting instance-based techniques for MDC is investigated, and a new approach named MD-kNN is proposed. Specifically, MD-kNN identifies unseen instance's k nearest neighbors and obtains its corresponding kNN counting statistics for each class space, based on which maximum a posteriori (MAP) inference is made for each pair of class spaces. After that, the class label w.r.t. each class space is determined by synergizing predictions from the learned classifiers via consulting empirical kNN accuracy. Comparative studies over ten benchmark data sets clearly validate MD-kNN's effectiveness.

Influence of Event Duration on Automatic Wheeze Classification

Bruno M Rocha, Diogo Pessoa, Alda Marques, Paulo Carvalho, Rui Pedro Paiva

Auto-TLDR; Experimental Design of the Non-wheeze Class for Wheeze Classification

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Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifiers on an open access respiratory sound database, with the best one reaching sensitivity and specificity values of 98% and 95%, respectively. Then, by changing one parameter in the design of the non-wheeze class, i.e., event duration, the best classifier only reached sensitivity and specificity values of 53% and 75%, respectively. These results demonstrate the importance of experimental design on the assessment of wheeze classification algorithms' performance.

Supervised Feature Embedding for Classification by Learning Rank-Based Neighborhoods

Ghazaal Sheikhi, Hakan Altincay

Auto-TLDR; Supervised Feature Embedding with Representation Learning of Rank-based Neighborhoods

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In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by the well-known word embedding technique, word2vec. Proposed embedding method is implemented as representative learning of rank-based neighborhoods. The notion of context words in word2vec is extended into neighboring instances within a given window. Neighborship is defined using ranks of instances rather than their values so that regions with different densities are captured properly. Each sample is represented by a unique one-hot vector whereas its neighbors are encoded by several two-hot vectors. The two-hot vectors are identical for neighboring samples of the same class. A feed-forward neural network with a continuous projection layer, then learns the mapping from one-hot vectors to multiple two-hot vectors. The hidden layer determines the reduced subspace for the train samples. The obtained transformation is then applied on test data to find a lower-dimensional representation. Proposed method is tested in classification problems on 10 UCI data sets. Experimental results confirm that the proposed method is effective in finding a discriminative representation of the features and outperforms several supervised embedding approaches in terms of classification performance.

Supervised Classification Using Graph-Based Space Partitioning for Multiclass Problems

Nicola Yanev, Ventzeslav Valev, Adam Krzyzak, Karima Ben Suliman

Auto-TLDR; Box Classifier for Multiclass Classification

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We introduce and investigate in multiclass setting an efficient classifier which partitions the training data by means of multidimensional parallelepipeds called boxes. We show that multiclass classification problem at hand can be solved by integrating the heuristic minimum clique cover approach and the k-nearest neighbor rule. Our algorithm is motivated an algorithm for partitioning a graph into a minimal number of maximal. The main advantage of the new classifier called Box classifier is that it optimally utilizes the geometrical structure of the training set by decomposing the l-class problem (l > 2) into l binary classification problems. We discuss computational complexity of the proposed Box classifier. The extensive experiments performed on the simulated and real data for binary and multiclass problems show that in almost all cases the Box classifier performs significantly better than k-NN, SVM and decision trees.

Bayesian Active Learning for Maximal Information Gain on Model Parameters

Kasra Arnavaz, Aasa Feragen, Oswin Krause, Marco Loog

Auto-TLDR; Bayesian assumptions for Bayesian classification

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The fact that machine learning models, despite their advancements, are still trained on randomly gathered data is proof that a lasting solution to the problem of optimal data gathering has not yet been found. In this paper, we investigate whether a Bayesian approach to the classification problem can provide assumptions under which one is guaranteed to perform at least as good as random sampling. For a logistic regression model, we show that maximal expected information gain on model parameters is a promising criterion for selecting samples, assuming that our classification model is well-matched to the data. Our derived criterion is closely related to the maximum model change. We experiment with data sets which satisfy this assumption to varying degrees to see how sensitive our performance is to the violation of our assumption in practice.

ILS-SUMM: Iterated Local Search for Unsupervised Video Summarization

Yair Shemer, Daniel Rotman, Nahum Shimkin

Auto-TLDR; ILS-SUMM: Iterated Local Search for Video Summarization

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In recent years, there has been an increasing interest in building video summarization tools, where the goal is to automatically create a short summary of an input video that properly represents the original content. We consider shot-based video summarization where the summary consists of a subset of the video shots which can be of various lengths. A straightforward approach to maximize the representativeness of a subset of shots is by minimizing the total distance between shots and their nearest selected shots. We formulate the task of video summarization as an optimization problem with a knapsack-like constraint on the total summary duration. Previous studies have proposed greedy algorithms to solve this problem approximately, but no experiments were presented to measure the ability of these methods to obtain solutions with low total distance. Indeed, our experiments on video summarization datasets show that the success of current methods in obtaining results with low total distance still has much room for improvement. In this paper, we develop ILS-SUMM, a novel video summarization algorithm to solve the subset selection problem under the knapsack constraint. Our algorithm is based on the well-known metaheuristic optimization framework -- Iterated Local Search (ILS), known for its ability to avoid weak local minima and obtain a good near-global minimum. Extensive experiments show that our method finds solutions with significantly better total distance than previous methods. Moreover, to indicate the high scalability of ILS-SUMM, we introduce a new dataset consisting of videos of various lengths.

Using Meta Labels for the Training of Weighting Models in a Sample-Specific Late Fusion Classification Architecture

Peter Bellmann, Patrick Thiam, Friedhelm Schwenker

Auto-TLDR; A Late Fusion Architecture for Multiple Classifier Systems

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The performance of multiple classifier systems can be significantly improved by the use of intelligent classifier combination approaches. In this study, we introduce a novel late fusion architecture, which can be interpreted as a combination of the well-known mixture of experts and stacked generalization methods. Our proposed method aggregates the outputs of classification models and corresponding sample-specific weighting models. A special feature of our proposed architecture is that each weighting model is trained on an individual set of meta labels. Using individual sets of meta labels allows each weighting model to separate regions, on which the predictions of the corresponding classification model can be associated to an estimated confidence value. We test our proposed architecture on a set of publicly available databases, including different benchmark data sets. The experimental evaluation shows the effectiveness and potential of our proposed method. Moreover, we discuss different approaches for further improvement of our proposed architecture.

Comparison of Stacking-Based Classifier Ensembles Using Euclidean and Riemannian Geometries

Vitaliy Tayanov, Adam Krzyzak, Ching Y Suen

Auto-TLDR; Classifier Stacking in Riemannian Geometries using Cascades of Random Forest and Extra Trees

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This paper considers three different classifier stacking algorithms: simple stacking, cascades of classifier ensembles and nonlinear version of classifier stacking based on classifier interactions. Classifier interactions can be expressed using classifier prediction pairwise matrix (CPPM). As a meta-learner for the last algorithm Convolutional Neural Networks (CNNs) and two other classifier stacking algorithms (simple classifier stacking and cascades of classifier ensembles) have been applied. This allows applying classical stacking and cascade-based recursive stacking in the Euclidean and the Riemannian geometries. The cascades of random forests (RFs) and extra trees (ETs) are considered as a forest-based alternative to deep neural networks [1]. Our goal is to compare accuracies of the cascades of RFs and CNN-based stacking or deep multi-layer perceptrons (MLPs) for different classifications problems. We use gesture phase dataset from UCI repository [2] to compare and analyze cascades of RFs and extra trees (ETs) in both geometries and CNN-based version of classifier stacking. This data set was selected because generally motion is considered as a nonlinear process (patterns do no lie in Euclidean vector space) in computer vision applications. Thus we can assess how good are forest-based deep learning and the Riemannian manifolds (R-manifolds) when applied to nonlinear processes. Some more datasets from UCI repository were used to compare the aforementioned algorithms to some other well-known classifiers and their stacking-based versions in both geometries. Experimental results show that classifier stacking algorithms in Riemannian geometry (R-geometry) are less dependent on some properties of individual classifiers (e.g. depth of decision trees in RFs or ETs) in comparison to Euclidean geometry. More independent individual classifiers allow to obtain R-manifolds with better properties for classification. Generally, accuracy of classification using classifier stacking in R-geometry is higher than in Euclidean one.

Automatic Tuberculosis Detection Using Chest X-Ray Analysis with Position Enhanced Structural Information

Hermann Jepdjio Nkouanga, Szilard Vajda

Auto-TLDR; Automatic Chest X-ray Screening for Tuberculosis in Rural Population using Localized Region on Interest

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For Tuberculosis (TB) detection beside the more expensive diagnosis solutions such as culture or sputum smear analysis one could consider the automatic analysis of the chest X-ray (CXR). This could mimic the lung region reading by the radiologist and it could provide a cheap solution to analyze and diagnose pulmonary abnormalities such as TB which often co- occurs with HIV. This software based pulmonary screening can be a reliable and affordable solution for rural population in different parts of the world such as India, Africa, etc. Our fully automatic system is processing the incoming CXR image by applying image processing techniques to detect the region on interest (ROI) followed by a computationally cheap feature extraction involving edge detection using Laplacian of Gaussian which we enrich by counting the local distribution of the intensities. The choice to ”zoom in” the ROI and look for abnormalities locally is motivated by the fact that some pulmonary abnormalities are localized in specific regions of the lungs. Later on the classifiers can decide about the normal or abnormal nature of each lung X-ray. Our goal is to find a simple feature, instead of a combination of several ones, -proposed and promoted in recent years’ literature, which can properly describe the different pathological alterations in the lungs. Our experiments report results on two publicly available data collections1, namely the Shenzhen and the Montgomery collection. For performance evaluation, measures such as area under the curve (AUC), and accuracy (ACC) were considered, achieving AUC = 0.81 (ACC = 83.33%) and AUC = 0.96 (ACC = 96.35%) for the Montgomery and Schenzen collections, respectively. Several comparisons are also provided to other state- of-the-art systems reported recently in the field.

Leveraging Sequential Pattern Information for Active Learning from Sequential Data

Raul Fidalgo-Merino, Lorenzo Gabrielli, Enrico Checchi

Auto-TLDR; Sequential Pattern Information for Active Learning

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This paper presents a novel active learning technique aimed at the selection of sequences for manual annotation from a database of unlabelled sequences. Supervised machine learning algorithms can employ these sequences to build better models than those based on using random sequences for training. The main contribution of the proposed method is the use of sequential pattern information contained in the database to select representative and diverse sequences for annotation. These two characteristics ensure the proper coverage of the instance space of sequences and, at the same time, avoids over-fitting the trained model. The approach, called SPIAL (Sequential Pattern Information for Active Learning), uses sequential pattern mining algorithms to extract frequently occurring sub-sequences from the database and evaluates how representative and diverse each sequence is, based on this information. The output is a list of sequences for annotation sorted by representativeness and diversity. The algorithm is modular and, unlike current techniques, independent of the features taken into account by the machine learning algorithm that trains the model. Experiments done on well-known benchmarks involving sequential data show that the models trained using SPIAL increase their convergence speed while reducing manual effort by selecting small sets of very informative sequences for annotation. In addition, the computation cost using SPIAL is much lower than for the state-of-the-art algorithms evaluated.

Bridging the Gap between Natural and Medical Images through Deep Colorization

Lia Morra, Luca Piano, Fabrizio Lamberti, Tatiana Tommasi

Auto-TLDR; Transfer Learning for Diagnosis on X-ray Images Using Color Adaptation

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Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancy all at once through pretrained model fine-tuning. In this work we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments show how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.

Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification

Federico Pollastri, Juan Maroñas, Federico Bolelli, Giulia Ligabue, Roberto Paredes, Riccardo Magistroni, Costantino Grana

Auto-TLDR; A Probabilistic Convolutional Neural Network for Immunofluorescence Classification in Renal Biopsy

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With this work we tackle immunofluorescence classification in renal biopsy, employing state-of-the-art Convolutional Neural Networks. In this setting, the aim of the probabilistic model is to assist an expert practitioner towards identifying the location pattern of antibody deposits within a glomerulus. Since modern neural networks often provide overconfident outputs, we stress the importance of having a reliable prediction, demonstrating that Temperature Scaling, a recently introduced re-calibration technique, can be successfully applied to immunofluorescence classification in renal biopsy. Experimental results demonstrate that the designed model yields good accuracy on the specific task, and that Temperature Scaling is able to provide reliable probabilities, which are highly valuable for such a task given the low inter-rater agreement.

Multi-Scale and Attention Based ResNet for Heartbeat Classification

Haojie Zhang, Gongping Yang, Yuwen Huang, Feng Yuan, Yilong Yin

Auto-TLDR; A Multi-Scale and Attention based ResNet for ECG heartbeat classification in intra-patient and inter-patient paradigms

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This paper presents a novel deep learning framework for the electrocardiogram (ECG) heartbeat classification. Although there have been some studies with excellent overall accuracy, these studies have not been very accurate in the diagnosis of arrhythmia classes especially such as supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB). In our work, we propose a Multi-Scale and Attention based ResNet for heartbeat classification in intra-patient and inter-patient paradigms respectively. Firstly, we extract shallow features from a convolutional layer. Secondly, the shallow features are sent into three branches with different convolution kernels in order to combine receptive fields of different sizes. Finally, fully connected layers are used to classify the heartbeat. Besides, we design a new attention mechanism based on the characteristics of heartbeat data. At last, extensive experiments on benchmark dataset demonstrate the effectiveness of our proposed model.

PIF: Anomaly detection via preference embedding

Filippo Leveni, Luca Magri, Giacomo Boracchi, Cesare Alippi

Auto-TLDR; PIF: Anomaly Detection with Preference Embedding for Structured Patterns

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We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-FOREST, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-FOREST is better at measuring arbitrary distances and isolate points in the preference space.

A Versatile Crack Inspection Portable System Based on Classifier Ensemble and Controlled Illumination

Milind Gajanan Padalkar, Carlos Beltran-Gonzalez, Matteo Bustreo, Alessio Del Bue, Vittorio Murino

Auto-TLDR; Lighting Conditions for Crack Detection in Ceramic Tile

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This paper presents a novel setup for automatic visual inspection of cracks in ceramic tile as well as studies the effect of various classifiers and height-varying illumination conditions for this task. The intuition behind this setup is that cracks can be better visualized under specific lighting conditions than others. Our setup, which is designed for field work with constraints in its maximum dimensions, can acquire images for crack detection with multiple lighting conditions using the illumination sources placed at multiple heights. Crack detection is then performed by classifying patches extracted from the acquired images in a sliding window fashion. We study the effect of lights placed at various heights by training classifiers both on customized as well as state-of-the-art architectures and evaluate their performance both at patch-level and image-level, demonstrating the effectiveness of our setup. More importantly, ours is the first study that demonstrates how height-varying illumination conditions can affect crack detection with the use of existing state-of-the-art classifiers. We provide an insight about the illumination conditions that can help in improving crack detection in a challenging real-world industrial environment.

Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Marija Ivanovska, Domen Tabernik, Danijel Skocaj, Janez Pers

Auto-TLDR; Evaluating Anomaly Detection Algorithms for Practical Applications

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Anomaly detection in complex data structures is oneof the most challenging problems in computer vision. In manyreal-world problems, for example in the quality control in modernmanufacturing, the anomalous samples are usually rare, resultingin (highly) imbalanced datasets. However, in current researchpractice, these scenarios are rarely modeled, and as a conse-quence, evaluation of anomaly detection algorithms often do notreproduce results that are useful for practical applications. First,even in case of highly unbalanced input data, anomaly detectionalgorithms are expected to significantly reduce the proportionof anomalous samples, detecting ”almost all” anomalous samples(with exact specifications depending on the target customer). Thisplaces high importance on only the small part of the ROC curve,possibly rendering the standard metrics such as AUC (AreaUnder Curve) and AP (Average Precision) useless. Second, thetarget of automatic anomaly detection in practical applicationsis significant reduction in manual work required, and standardmetrics are poor predictor of this feature. Finally, the evaluationmay produce erratic results for different randomly initializedtraining runs of the neural network, producing evaluation resultsthat may not reproduce well in practice. In this paper, we presentan evaluation methodology that avoids these pitfalls.

Feature Engineering and Stacked Echo State Networks for Musical Onset Detection

Peter Steiner, Azarakhsh Jalalvand, Simon Stone, Peter Birkholz

Auto-TLDR; Echo State Networks for Onset Detection in Music Analysis

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In music analysis, one of the most fundamental tasks is note onset detection - detecting the beginning of new note events. As the target function of onset detection is related to other tasks, such as beat tracking or tempo estimation, onset detection is the basis for such related tasks. Furthermore, it can help to improve Automatic Music Transcription (AMT). Typically, different approaches for onset detection follow a similar outline: An audio signal is transformed into an Onset Detection Function (ODF), which should have rather low values (i.e. close to zero) for most of the time but with pronounced peaks at onset times, which can then be extracted by applying peak picking algorithms on the ODF. In the recent years, several kinds of neural networks were used successfully to compute the ODF from feature vectors. Currently, Convolutional Neural Networks (CNNs) define the state of the art. In this paper, we build up on an alternative approach to obtain a ODF by Echo State Networks (ESNs), which have achieved comparable results to CNNs in several tasks, such as speech and image recognition. In contrast to the typical iterative training procedures of deep learning architectures, such as CNNs or networks consisting of Long-Short-Term Memory Cells (LSTMs), in ESNs only a very small part of the weights is easily trained in one shot using linear regression. By comparing the performance of several feature extraction methods, pre-processing steps and introducing a new way to stack ESNs, we expand our previous approach to achieve results that fall between a bidirectional LSTM network and a CNN with relative improvements of 1.8% and -1.4%, respectively. For the evaluation, we used exactly the same 8-fold cross validation setup as for the reference results.