Kernel logistic PLS: a new tool for complex classification

[TGS05] Kernel logistic PLS: a new tool for complex classification

Conférence Internationale avec comité de lecture : ASMDA'05 XIth Int. Symp. on Applied Stochastic Models and Data Analysis. Brest, France, January 2005, pp.441-451,

Auteurs: A. Tenenhaus , A. Giron , G. Saporta , B. Fertil

motcle:

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 competitiveness with 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 computational charges of the SVM training algorithm without significant loss of accuracy.

Download

@inproceedings {
	TGS05,
	title	=	"{Kernel logistic PLS: a new tool for complex classification}",
	author	=	" A. Tenenhaus and A. Giron and G. Saporta and B. Fertil ",
	booktitle	=	"{ASMDA'05 XIth Int. Symp. on Applied Stochastic Models and Data Analysis. Brest, France}",
	year	=	2005,
	month	=	"January",
	pages	=	"441-451",
}