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Modélisation probabiliste de classifieurs d’ensemble pour des problèmes à deux classes

Abstract : The objective of this thesis is to improve or maintain the performance of a decision-making system when the environment can impact some attributes of the feature space at a given time or depending on the geographical location of the observation. Inspired by ensemble methods, our approach has been to make decisions in representation sub-spaces resulting of projections of the initial space, expecting that most of the subspaces are not impacted. The final decision is then made by fusing the individual decisions. In this context, three classification methods (one-class SVM, Kernel PCA and Kernel ECA) were tested on a textured images segmentation problem which is a perfectly adequate application support because of texture pattern changes at the border between two regions. Then, we proposed a new fusion rule based on a likelihood ratio test for a set of independent classifiers. Compared to the majority vote, this fusion rule showed better performance against the alteration of the performance space. Finally, we modeled the decision system using a joint model for all decisions based on the assumption that decisions of individual classifiers follow a correlated Bernoulli law. This model is intended to link the performance of individual classifiers to the performance of the overall decision rule and to investigate and control the impact of changes in the original space on the overall performance
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Submitted on : Tuesday, October 13, 2020 - 2:43:07 PM
Last modification on : Wednesday, October 14, 2020 - 3:29:29 AM


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  • HAL Id : tel-02965727, version 1




Yuan Dong. Modélisation probabiliste de classifieurs d’ensemble pour des problèmes à deux classes. Intelligence artificielle [cs.AI]. Université de Technologie de Troyes, 2013. Français. ⟨NNT : 2013TROY0013⟩. ⟨tel-02965727⟩



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