A robust classification method using combined classifiers in a nonstationary environment

Abstract : In this paper, we present a robust data classification method based on an ensemble of feature subspaces. The objective is to improve or preserve the performances of a decisional system in the case of perturbations due to noise or sensor degradation. The proposed method is to combine a set of classifiers each of which is established in the corresponding feature subspace resulting from projections of the initial full-dimensional space, expecting that most of them are not impaired. The counterpart of the expected robustness is a performance decrease for non-impaired data. In this context, three classification methods are tested, One-class SVM, Kernel PCA and Kernel ECA, to study the robustness of the final decision. The results obtained in textured image segmentation demonstrate that our approach is efficient in a nonstationary environment.
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02290408
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, September 17, 2019 - 4:04:47 PM
Last modification on : Wednesday, September 18, 2019 - 1:28:48 AM

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  • HAL Id : hal-02290408, version 1

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Yuan Dong, Pierre Beauseroy, André Smolarz. A robust classification method using combined classifiers in a nonstationary environment. 20th European Signal Processing Conference (EUSIPCO), Aug 2012, Bucharest, Romania. ⟨hal-02290408⟩

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