Combinaison crédibiliste de classifieurs binaires

The problem of binary classifier combination is adressed in this article. This approach consists in solving a multi-class classification problem by combining the solutions of binary sub-problems. We consider two strategies in which each class is opposed to each other, or to all others. The combination is considered from the point of view of the theory of evidence. The classifier outputs are interpreted either as conditional belief functions, or as belief functions expressed in a coarser frame. They are combined by computing a belief function that is consistent with the available information. The performances of the methods are compared with those of other techniques and illustrated on various datasets.Nous étudions dans cet article le problème de la combinaison de classifieurs binaires. Cette approche consiste à résoudre un problème de discrimination multi-classes, en combinant les solutions de sous-problèmes binaires ; nous nous intéressons aux stratégies opposant chaque classe à chaque autre, et chaque classe à toutes les autres. La combinaison est considérée ici du point de vue de la théorie de Dempster-Shafer : les sorties des classifieurs sont ainsi interprétées comme des fonctions de croyance, conditionnelles ou exprimées dans un cadre plus grossier que le cadre initial. Elles sont combinées en calculant une fonction de croyance consistante avec les informations disponibles. Les performances des deux approches sont comparées à celles d’autres méthodes et illustrées sur divers jeux de données

Moving object detection and segmentation in urban environments from a moving platform

This paper proposes an effective approach to detect and segment moving objects from two time-consecutive stereo frames, which leverages the uncertainties in camera motion estimation and in disparity computation. First, the relative camera motion and its uncertainty are computed by tracking and matching sparse features in four images. Then, the motion likelihood at each pixel is estimated by taking into account the ego-motion uncertainty and disparity in computation procedure. Finally, the motion likelihood, color and depth cues are combined in the graph-cut framework for moving object segmentation. The efficiency of the proposed method is evaluated on the KITTI benchmarking datasets, and our experiments show that the proposed approach is robust against both global (camera motion) and local (optical flow) noise. Moreover, the approach is dense as it applies to all pixels in an image, and even partially occluded moving objects can be detected successfully. Without dedicated tracking strategy, our approach achieves high recall and comparable precision on the KITTI benchmarking sequences.This work was carried out within the framework of the Equipex ROBOTEX (ANR-10- EQPX-44-01). Dingfu Zhou was sponsored by the China Scholarship Council for 3.5 year’s PhD study at HEUDIASYC laboratory in University of Technology of Compiegne

LFA 2022 : Actes des 31èmes rencontres francophones sur la logique floue et ses applications

Les rencontres francophones sur la Logique Floue et ses Applications (LFA) constituent la manifestation scientifique francophone annuelle où chercheurs académiques et industriels se réunissent afin de faire le point sur les développements récents des théories de l’imprécis et de l’incertain. Celles-ci comprennent, par exemple, les sous-ensembles flous, les possibilités, les fonctions de croyance, les probabilités imprécises, les ensembles approximatifs et aléatoires ou les logiques non classiques. Le large éventail de domaines couverts va de la commande floue, domaine historique de l’application des sous-ensembles flous, à l’apprentissage automatique, la fouille données et la classification, en passant par l’aide à la décision, le raisonnement, l’agrégation et la fusion d’informations, les bases de données ou encore l’explicabilité en Intelligence Artificielle (XAI), pour n’en citer que quelques-uns. Les actes de l’édition 2022 de ces rencontres comprennent une sélection de trente articles qui couvrent assez largement l’ensemble de ces thématiques classiques et émergentes. À ceux-ci, s’ajoutent trois conférences invitées sur des thématiques d’actualité. Serena Villata, directrice de recherche au CNRS, laboratoire I3S, Université Côte d'Azur (Nice), aborde l'évaluation de la fiabilité et de la qualité des arguments dans le cadre des systèmes artificiels d'argumentation. Eyke Hüllermeier, professeur à l'institut d'informatique, Ludwig-Maximilians Universität (Munich, Allemagne), traite de la représentation et de la quantification de l'incertitude en apprentissage machine. Pawel Zielinski, professeur au département d'informatique fondamentale, université des sciences et technologies (Wroclaw, Pologne), décrit un cadre pour l'optimisation possibiliste robuste aux hypothèses de distribution.

Combining binary classifiers with imprecise probabilities

This paper proposes a simple framework to combine binary classifiers whose outputs are imprecise probabilities (or are transformed into some imprecise probabilities, e.g., by using confidence intervals). This combination comes down to solve linear programs describing constraints over events (here, subsets of classes). The number of constraints grows linearly with the number of classifiers, making the proposed framework tractable even for problems involving a relatively large number of classes

Combinaison de classifieurs binaires dans le cadre des fonctions de croyance

La classification supervisée a pour enjeu de construire un système, ou classifieur, capable de prédire automatiquement la classe d'un phénomène observé. Son architecture peut être modulaire : le problème abordé est décomposé en sous-problèmes plus simples, traités par des classifieurs, et la combinaison des résultats donne la solution globale. Nous nous intéressons au cas de sous-problèmes binaires, en particulier les décompositions où chaque classe est opposée à chaque autre, chaque classe est opposée à toutes les autres, et le cas général où deux groupes de classes disjoints sont opposés l'un à l'autre. La combinaison des classifieurs est formalisée dans le cadre de la théorie des fonctions de croyance. Nous interprétons les sorties des classifieurs binaires comme des fonctions de croyance définies sur des domaines restreints, dépendant du schéma de décomposition employée. Les classifieurs sont alors combinés en déterminant la fonction de croyance la plus consistante possible avec leurs sorties.Supervised classification aims at building a system, or classifier, able to predict the class of a phenomenon being observed. Its architecture may be modular : the problem to be tackled is decomposed into simpler sub-problems, solved by classifiers, and the combination of the results gives the global solution. We address the case of binary sub-problems in particular the decompositions where each class is opposed to each other, each class is opposed to an the others, and the general case where two disjoint groups of classes are opposed to each other. The combination of the classifiers is formalized within the theory of evidence framework. We interpret the outputs of the binary classifiers as belief functions defined on restricted domains, according to the decomposition scheme used. The classifiers are then combined by determining the belief function which is the most ... consistant with their outputs.COMPIEGNE-BU (601592101) / SudocSudocFranceF

Clustering and classification of fuzzy data using the fuzzy EM algorithm

International audienceIn this article, we address the problem of clustering imprecise data using a finite mixture of Gaussians. We propose to estimate the parameters of the model using the fuzzy EM algorithm. This extension of the EM algorithm allows us to handle imprecise data represented by fuzzy numbers. First, we briefly recall the principle of the fuzzy EM algorithm. Then, we provide closed-forms for the parameter estimates in the case of Gaussian fuzzy data. We also describe a Monte-Carlo procedure for estimating the parameter updates in the general case. Experiments carried out on synthetic and real data demonstrate the interest of our approach for taking into account attribute and label uncertainty

Soft Label Based Semi-Supervised Boosting for Classification and Object Recognition

International audienceSupervised classification algorithms such as Boosting and SVM have achieved significant success in the field of computer vision for classification and object recognition. However, the performance of the classifier decreases rapidly if there are insufficient labelled training samples. In this paper, a semi-supervised boosting algorithm is proposed to overcome this limitation. First, a few labelled instances are use to estimate probabilistic class labels for unlabelled samples using Gaussian Mixture Models after a dimension reduction step performed via Principal Component Analysis. Then, we apply a boosting strategy on decision stumps trained using the soft labelled instances thus obtained. The performances of our strategy are evaluated on several state-of-the-art classification datasets, as well as on a pedestrian detection and recognition problem. Experimental results demonstrate the interest of taking into account additional data in the training process

Parametric classification with soft labels using the Evidential EM algorithm : linear discriminant analysis versus logistic regression

International audiencePartially supervised learning extends both supervised and unsu-pervised learning, by considering situations in which only partial information about the response variable is available. In this paper, we consider partially supervised classification and we assume the learning instances to be labeled by Dempster-Shafer mass functions, called soft labels. Linear discriminant analysis and logistic regression are considered as special cases of generative and discriminative parametric models. We show that the evidential EM algorithm can be particularized to fit the parameters in each of these models. We describe experimental results with simulated data sets as well as with two real applications: K-complex detection in sleep EEGs signals and facial expression recognition. These results confirm the interest of using soft labels for classification as compared to potentially erroneous crisp labels, when the true class membership is partially unknown or ill-defined

Pairwise classifier combination in the transferable belief model

Abstract – Classifier combination constitutes an interesting approach when solving multi-class classification problems. We propose to carry out this combination in the belief functions framework. Our approach, similar to a method proposed by Hastie and Tibshirani in a probabilistic framework, is first presented. The performances obtained on various datasets are then analyzed, showing a gain of classification accuracy using the belief functions approach