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A robust classification method using combined classifiers in a nonstationary environment

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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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Dates and versions

hal-02290408 , version 1 (17-09-2019)

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  • HAL Id : hal-02290408 , version 1

Cite

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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