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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

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.

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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.

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.

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.

Minority Class Oriented Active Learning for Imbalanced Datasets

Umang Aggarwal, Adrian Popescu, Celine Hudelot

Auto-TLDR; Active Learning for Imbalanced Datasets

Abstract Slides Poster Similar

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.

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.

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.

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

Abstract Slides Poster Similar

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.

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

Abstract Slides Poster Similar

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.

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 Similar

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.

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.

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

Abstract Slides Poster Similar

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.

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.

Explainable Online Validation of Machine Learning Models for Practical Applications

Wolfgang Fuhl, Yao Rong, Thomas Motz, Michael Scheidt, Andreas Markus Hartel, Andreas Koch, Enkelejda Kasneci

Auto-TLDR; A Reformulation of Regression and Classification for Machine Learning Algorithm Validation

Abstract Slides Poster Similar

We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original problem and validates the result of the machine learning algorithm using the training data. Since the validation of machine learning algorithms must always be explainable, we perform our experiments with the kNN algorithm as well as with an algorithm based on conditional probabilities, which is proposed in this work. For the evaluation of our approach, three publicly available data sets were used and three classification and two regression problems were evaluated. The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.

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.

Neuron-Based Network Pruning Based on Majority Voting

Ali Alqahtani, Xianghua Xie, Ehab Essa, Mark W. Jones

Auto-TLDR; Large-Scale Neural Network Pruning using Majority Voting

Abstract Slides Poster Similar

The achievement of neural networks in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose an efficient method to simultaneously identify the critical neurons and prune the model during training without involving any pre-training or fine-tuning procedures. Unlike existing methods, which accomplish this task in a greedy fashion, we propose a majority voting technique to compare the activation values among neurons and assign a voting score to quantitatively evaluate their importance.This mechanism helps to effectively reduce model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Experimental results show that majority voting efficiently compresses the network with no drop in model accuracy, pruning more than 79\% of the original model parameters on CIFAR10 and more than 91\% of the original parameters on MNIST. Moreover, we show that with our proposed method, sparse models can be further pruned into even smaller models by removing more than 60\% of the parameters, whilst preserving the reference model accuracy.

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

Abstract Slides Poster Similar

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.

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

Abstract Slides Poster Similar

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.

IDA-GAN: A Novel Imbalanced Data Augmentation GAN

Hao Yang, Yun Zhou

Auto-TLDR; IDA-GAN: Generative Adversarial Networks for Imbalanced Data Augmentation

Abstract Slides Poster Similar

Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significantly deteriorate the accuracy of classification. To address this challenge, we propose a novel Imbalanced Data Augmentation Generative Adversarial Networks (GAN) named IDA-GAN as an augmentation tool to deal with the imbalanced dataset. This is a great challenge because it is hard to train a GAN model under this situation. We overcome this issue by coupling Variational autoencoder along with GAN training. Specifically, we introduce the Variational autoencoder to learn the majority and minority class distributions in the latent space, and use the generative model to utilize each class distribution for the subsequent GAN training. The generative model learns useful features to generate target minority-class samples. By comparing with the state-of-the-art GAN models, the experimental results demonstrate that our proposed IDA-GAN could generate more diverse minority samples with better qualities, and it consistently benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets: MNIST, Fashion-MNIST, SVHN, CIFAR-10 and GTRSB.

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

Abstract Slides Poster Similar

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.

Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study

Veysel Kocaman, Ofer M. Shir, Thomas Baeck

Auto-TLDR; Exploiting Batch Normalization before the Output Layer in Deep Learning for Minority Class Detection in Imbalanced Data Sets

Abstract Slides Poster Similar

Some real-world domains, such as Agriculture and Healthcare, comprise early-stage disease indications whose recording constitutes a rare event, and yet, whose precise detection at that stage is critical. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. To simulate such scenarios, we artificially generate skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset as a basis for classification of scarce visual cues through transfer learning. By randomly and unevenly picking healthy and unhealthy samples from certain plant types to form a training set, we consider a base experiment as fine-tuning ResNet34 and VGG19 architectures and then testing the model performance on a balanced dataset of healthy and unhealthy images. We empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19. We demonstrate that utilizing an additional BN layer before the output layer in modern CNN architectures has a considerable impact in terms of minimizing the training time and testing error for minority classes in highly imbalanced data sets. Moreover, when the final BN is employed, trying to minimize validation and training losses may not be an optimal way for getting a high F1 test score for minority classes in anomaly detection problems. That is, the network might perform better even if it is not ‘confident’ enough while making a prediction; leading to another discussion about why softmax output is not a good uncertainty measure for DL models.

Deep Learning on Active Sonar Data Using Bayesian Optimization for Hyperparameter Tuning

Henrik Berg, Karl Thomas Hjelmervik

Auto-TLDR; Bayesian Optimization for Sonar Operations in Littoral Environments

Abstract Slides Poster Similar

Sonar operations in littoral environments may be challenging due to an increased probability of false alarms. Machine learning can be used to train classifiers that are able to filter out most of the false alarms automatically, however, this is a time consuming process, with many hyperparameters that need to be tuned in order to yield useful results. In this paper, Bayesian optimization is used to search for good values for some of the hyperparameters, like topology and training parameters, resulting in performance superior to earlier trial-and-error based training. Additionally, we analyze some of the parameters involved in the Bayesian optimization, as well as the resulting hyperparameter values.

Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

Christian Haase-Schütz, Rainer Stal, Heinz Hertlein, Bernhard Sick

Auto-TLDR; Meta Training and Labelling for Unlabelled Data

Abstract Slides Poster Similar

State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of combining self-training with pseudolabels, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling. Furthermore, our approach can be used for semi-supervised learning.

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.

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.

On Learning Random Forests for Random Forest Clustering

Manuele Bicego, Francisco Escolano

Auto-TLDR; Learning Random Forests for Clustering

Abstract Slides Poster Similar

In this paper we study the poorly investigated problem of learning Random Forests for distance-based Random Forest clustering. We studied both classic schemes as well as alternative approaches, novel in this context. In particular, we investigated the suitability of Gaussian Density Forests, Random Forests specifically designed for density estimation. Further, we introduce a novel variant of Random Forest, based on an effective non parametric by-pass estimator of the Renyi entropy, which can be useful when the parametric assumption is too strict. An empirical evaluation involving different datasets and different RF-clustering strategies confirms that the learning step is crucial for RF-clustering. We also present a set of practical guidelines useful to determine the most suitable variant of RF-clustering according to the problem under examination.

Attribute-Based Quality Assessment for Demographic Estimation in Face Videos

Fabiola Becerra-Riera, Annette Morales-González, Heydi Mendez-Vazquez, Jean-Luc Dugelay

Auto-TLDR; Facial Demographic Estimation in Video Scenarios Using Quality Assessment

Abstract Slides Similar

Most existing works regarding facial demographic estimation are focused on still image datasets, although nowadays the need to analyze video content in real applications is increasing. We propose to tackle gender, age and ethnicity estimation in the context of video scenarios. Our main contribution is to use an attribute-specific quality assessment procedure to select best quality frames from a video sequence for each of the three demographic modalities. Best quality frames are classified with fine-tuned MobileNet models and a final video prediction is obtained with a majority voting strategy among the best selected frames. Our validation on three different datasets and our comparison with state-of-the-art models, show the effectiveness of the proposed demographic classifiers and the quality pipeline, which allows to reduce both: the number of frames to be classified and the processing time in practical applications; and improves the soft biometrics prediction accuracy.

How to Define a Rejection Class Based on Model Learning?

Sarah Laroui, Xavier Descombes, Aurelia Vernay, Florent Villiers, Francois Villalba, Eric Debreuve

Auto-TLDR; An innovative learning strategy for supervised classification that is able, by design, to reject a sample as not belonging to any of the known classes

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In supervised classification, the learning process typically trains a classifier to optimize the accuracy of classifying data into the classes that appear in the learning set, and only them. While this framework fits many use cases, there are situations where the learning process is knowingly performed using a learning set that only represents the data that have been observed so far among a virtually unconstrained variety of possible samples. It is then crucial to define a classifier which has the ability to reject a sample, i.e., to classify it into a rejection class that has not been yet defined. Although obvious solutions can add this ability a posteriori to a classifier that has been learned classically, a better approach seems to directly account for this requirement in the classifier design. In this paper, we propose an innovative learning strategy for supervised classification that is able, by design, to reject a sample as not belonging to any of the known classes. For that, we rely on modeling each class as the combination of a probability density function (PDF) and a threshold that is computed with respect to the other classes. Several alternatives are proposed and compared in this framework. A comparison with straightforward approaches is also provided.

Watermelon: A Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy

Xiang Xie, Wilhelm Stork

Auto-TLDR; Feature Selection Using Bayes Error Rate Estimation for Dynamic Feature Selection

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Feature selection has become a crucial part of many classification problems in which high-dimensional datasets may contain tens of thousands of features. In this paper, we propose a novel feature selection method scoring the features through estimating the Bayes error rate based on kernel density estimation. Additionally, we update the scores of features dynamically by quantitatively interpreting the effects of feature relevance and redundancy in a new way. Distinguishing from the common heuristic applied by many feature selection methods, which prefers choosing features that are not relevant to each other, our approach penalizes only monotonically correlated features and rewards any other kind of relevance among features based on Spearman’s rank correlation coefficient and normalized mutual information. We conduct extensive experiments on seventeen diverse classification benchmarks, the results show that our approach overperforms other seventeen popular state-of-the-art feature selection methods in most cases.

CNN-Based Repetitive Self-Revised Learning for Photos’ Aesthetics Imbalanced Classification

Dai Ying

Auto-TLDR; Automatic Aesthetic Assessment Using Recurrent Self-revised Learning

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Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set. As RSRL, the network is trained repetitively by dropping out the low likelihood photo samples at the middle levels of aesthetics from the training data set based on the previously trained network. Further, the retained two networks are used in extracting highlight regions of the photos related with the aesthetic assessment. Experimental results show that the CNN-based repetitive self-revised learning is effective for improving the performances of the imbalanced classification.

Rank-Based Ordinal Classification

Joan Serrat, Idoia Ruiz

Auto-TLDR; Ordinal Classification with Order

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Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class {\em labels}. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1\ldots $C$, being $C$ the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to less likely. This is tanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, \textit{i.e.}, most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset.

ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map

Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Jun Ho Huh, Choong-Hoon Lee, Youngbae Jeon, Jeonghwan Hwang, Ji Won Yoon

Auto-TLDR; ASVspoof 2019: A Lightweight Automatic Speaker Verification Spoofing and Countermeasures System

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The ``2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge'' (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy -- this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019 dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, outperforming the top ensemble system from the competition that achieved an EER of 0.39%.

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.

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.

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

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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.

Knowledge Distillation Beyond Model Compression

Fahad Sarfraz, Elahe Arani, Bahram Zonooz

Auto-TLDR; Knowledge Distillation from Teacher to Student

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Knowledge distillation (KD) is commonly deemed as an effective model compression technique in which a compact model (student) is trained under the supervision of a larger pretrained model or an ensemble of models (teacher). Various techniques have been proposed since the original formulation, which mimics different aspects of the teacher such as the representation space, decision boundary or intra-data relationship. Some methods replace the one way knowledge distillation from a static teacher with collaborative learning between a cohort of students. Despite the recent advances, a clear understanding of where knowledge resides in a deep neural network and optimal method for capturing knowledge from teacher and transferring it to student still remains an open question. In this study we provide an extensive study on 9 different knowledge distillation methods which covers a broad spectrum of approaches to capture and transfer knowledge. We demonstrate the versatility of the KD framework on different datasets and network architectures under varying capacity gaps between the teacher and student. The study provides intuition for the effects of mimicking different aspects of the teacher and derives insights from the performance of the different distillation approaches to guide the the design of more effective KD methods . Furthermore, our study shows the effectiveness of the KD framework in learning efficiently under varying severity levels of label noise and class imbalance, consistently providing significant generalization gains over standard training. We emphasize that the efficacy of KD goes much beyond a model compression technique and should be considered as a general purpose training paradigm which offers more robustness to common challenges in the real-world datasets compared to the standard training procedure.

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

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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.

Uncertainty-Aware Data Augmentation for Food Recognition

Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khatun, Marc Bolaños, Petia Radeva

Auto-TLDR; Data Augmentation for Food Recognition Using Epistemic Uncertainty

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Food recognition has recently attracted attention of many researchers. However, high food ambiguity, inter-class variability and intra-class similarity define a real challenge for the Deep learning and Computer Vision algorithms. In order to improve their performance, it is necessary to better understand what the model learns and, from this, to determine the type of data that should be additionally included for being the most beneficial to the training procedure. In this paper, we propose a new data augmentation strategy that estimates and uses the epistemic uncertainty to guide the model training. The method follows an active learning framework, where the new synthetic images are generated from the hard to classify real ones present in the training data based on the epistemic uncertainty. Hence, it allows the food recognition algorithm to focus on difficult images in order to learn their discriminatives features. On the other hand, avoiding data generation from images that do not contribute to the recognition makes it faster and more efficient. We show that the proposed method allows to improve food recognition and provides a better trade-off between micro- and macro-recall measures.

A Close Look at Deep Learning with Small Data

Lorenzo Brigato, Luca Iocchi

Auto-TLDR; Low-Complex Neural Networks for Small Data Conditions

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In this work, we perform a wide variety of experiments with different Deep Learning architectures in small data conditions. We show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we improve the state of the art using low complexity models. We show that standard convolutional neural networks with relatively few parameters are effective in this scenario. In many of our experiments, low complexity models outperform state-of-the-art architectures. Moreover, we propose a novel network that uses an unsupervised loss to regularize its training. Such architecture either improves the results either performs comparably well to low capacity networks. Surprisingly, experiments show that the dynamic data augmentation pipeline is not beneficial in this particular domain. Statically augmenting the dataset might be a promising research direction while dropout maintains its role as a good regularizer.

Multi-annotator Probabilistic Active Learning

Marek Herde, Daniel Kottke, Denis Huseljic, Bernhard Sick

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

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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.

Learning Parameter Distributions to Detect Concept Drift in Data Streams

Johannes Haug, Gjergji Kasneci

Auto-TLDR; A novel framework for the detection of concept drift in streaming environments

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Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and robust identification of concept drift can be difficult, as we never have access to the true distribution of streaming data. In this work, we propose a novel framework for the detection of real concept drift, called ERICS. By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters. To this end, we adopt common measures from information theory. The proposed framework is completely model-agnostic. By choosing an appropriate base model, ERICS is also capable to detect concept drift at the input level, which is a significant advantage over existing approaches. An evaluation on several synthetic and real-world data sets suggests that the proposed framework identifies concept drift more effectively and precisely than various existing works.

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

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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.

Personalized Models in Human Activity Recognition Using Deep Learning

Hamza Amrani, Daniela Micucci, Paolo Napoletano

Auto-TLDR; Incremental Learning for Personalized Human Activity Recognition

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Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the classifiers thanks to a continuous learning based on user data. Finally, deep learning techniques have been proven to be more effective with respect to traditional ones in the generation of user-independent models. The aim of our work is therefore to put together deep learning techniques with incremental learning in order to obtain personalized models that perform better with respect to user-independent model and personalized model obtained using traditional machine learning techniques. The experimentation was done by comparing the results obtained by a technique in the state of the art with those obtained by two neural networks (ResNet and a simplified CNN) on three datasets. The experimentation showed that neural networks adapt faster to a new user than the baseline.

Aggregating Dependent Gaussian Experts in Local Approximation

Hamed Jalali, Gjergji Kasneci

Auto-TLDR; A novel approach for aggregating the Gaussian experts by detecting strong violations of conditional independence

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Distributed Gaussian processes (DGPs) are prominent local approximation methods to scale Gaussian processes (GPs) to large datasets. Instead of a global estimation, they train local experts by dividing the training set into subsets, thus reducing the time complexity. This strategy is based on the conditional independence assumption, which basically means that there is a perfect diversity between the local experts. In practice, however, this assumption is often violated, and the aggregation of experts leads to sub-optimal and inconsistent solutions. In this paper, we propose a novel approach for aggregating the Gaussian experts by detecting strong violations of conditional independence. The dependency between experts is determined by using a Gaussian graphical model, which yields the precision matrix. The precision matrix encodes conditional dependencies between experts and is used to detect strongly dependent experts and construct an improved aggregation. Using both synthetic and real datasets, our experimental evaluations illustrate that our new method outperforms other state-of-the-art (SOTA) DGP approaches while being substantially more time-efficient than SOTA approaches, which build on independent experts.

EasiECG: A Novel Inter-Patient Arrhythmia Classification Method Using ECG Waves

Chuanqi Han, Ruoran Huang, Fang Yu, Xi Huang, Li Cui

Auto-TLDR; EasiECG: Attention-based Convolution Factorization Machines for Arrhythmia Classification

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Abstract—In an ECG record, the PQRST waves are of important medical significance which provide ample information reflecting heartbeat activities. In this paper, we propose a novel arrhythmia classification method namely EasiECG, characterized by simplicity and accuracy. Compared with other works, the EasiECG takes the configuration of these five key waves into account and does not require complicated feature engineering. Meanwhile, an additional encoding of the extracted features makes the EasiECG applicable even on samples with missing waves. To automatically capture interactions that contribute to the classification among the processed features, a novel adapted classification model named Attention-based Convolution Factorization Machines (ACFM) is proposed. In detail, the ACFM can learn both linear and high-order interactions from linear regression and convolution on outer-product feature interaction maps, respectively. After that, an attention mechanism implemented in the model can further assign different importance of these interactions when predicting certain types of heartbeats. To validate the effectiveness and practicability of our EasiECG, extensive experiments of inter-patient paradigm on the benchmark MIT-BIH arrhythmia database are conducted. To tackle the imbalanced sample problem in this dataset, an ingenious loss function: focal loss is adopted when training. The experiment results show that our method is competitive compared with other state-of-the-arts, especially in classifying the Supraventricular ectopic beats. Besides, the EasiECG achieves an overall accuracy of 87.6% on samples with a missing wave in the related experiment, demonstrating the robustness of our proposed method.

Drift Anticipation with Forgetting to Improve Evolving Fuzzy System

Clément Leroy, Eric Anquetil, Nathalie Girard

Auto-TLDR; A coherent method to integrate forgetting in Evolving Fuzzy System

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Working with a non-stationary stream of data requires for the analysis system to evolve its model (the parameters as well as the structure) over time. In particular, concept drifts can occur, which makes it necessary to forget knowledge that has become obsolete. However, the forgetting is subjected to the plasticity stability dilemma. It says that increase forgetting improve reactivity of the adaptation to the new data while reducing the robustness of the system. Based on a set of inference rules, Evolving Fuzzy Systems - EFS - have proven to be effective in addressing the data stream learning problem. However tackling the stability plasticity dilemma is still an open question. This paper proposes a coherent method to integrate forgetting in Evolving Fuzzy System, based on the recently introduced notion of concept drift anticipation. The forgetting is applied with two methods: an exponential forgetting of the premise part and a differed directional forgetting of the conclusion part of EFS to preserve the coherence between both parts. The originality of the approach consists in applying the forgetting only in the anticipation module and in keeping the EFS (called principal system) learned without any forgetting. Then, when a drift is detected in the stream, a selection mechanism is proposed to replace the obsolete parameters of the principal system with more suitable parameters of the anticipation module. An evaluation of the proposed methods is carried out on benchmark online datasets, with a comparison with state-of-the-art online classifiers (Learn++.NSE, PENsemble, pclass) as well as with the original system using different forgetting strategies.

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.

Electroencephalography Signal Processing Based on Textural Features for Monitoring the Driver’s State by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis

Auto-TLDR; One-dimensional Local Binary Pattern Algorithm for Estimating Driver Vigilance in a Brain-Computer Interface System

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In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.