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Communication Dans Un Congrès Année : 2005

Kernel logistic PLS: a new tool for complex classification

Résumé

Kernel Logistic PLS” (KL-PLS), a new tool for classification with performances similar to the most powerful statistical methods is described in this paper. KL-PLS is based on the principles of PLS generalized regression and learning via kernel. The successions of simple regressions, simple logistic regression and multiple logistic regressions on a small number of uncorrelated variables that are computed within KL-PLS algorithm are convenient for the management of very high dimensional data. The algorithm was applied to a variety of benchmark data sets for classification and in all cases, KL-PLS demonstrates its competitivenesswith other state-of-art classification method. Furthermore, leaning on statistical tests related to the logistic regression, KL-PLS allows the systematic detection of data points close to “support vectors” of SVM and thus reduces the computationalcharges of the SVM training algorithm without significant loss of accuracy.
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Dates et versions

hal-01125042 , version 1 (30-03-2020)

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  • HAL Id : hal-01125042 , version 1

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Arthur Tenenhaus, Alain Giron, Gilbert Saporta, Bernard Fertil. Kernel logistic PLS: a new tool for complex classification. ASMDA'05 XIth Int. Symp. on Applied Stochastic Models and Data Analysis, May 2005, Brest, France. pp.441-451. ⟨hal-01125042⟩
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