Variable Selection for Kernel Classification

被引：0

作者：

Steel, S. J. ^{[1
]}

Louw, N. ^{[1
]}

Bierman, S. ^{[1
]}

机构：

[1] Univ Stellenbosch, Dept Stat & Actuarial Sci, ZA-7602 Matieland, South Africa

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2011年 / 40卷 / 02期

关键词：

Kernel Fisher discriminant analysis; Support vector machines; Surrogate selection; Wide data sets; COLON-CANCER PREDICTION; REGRESSION;

D O I：

10.1080/03610918.2010.534226

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this article, a variable selection procedure, called surrogate selection, is proposed which can be applied when a support vector machine or kernel Fisher discriminant analysis is used in a binary classification problem. Surrogate selection applies the lasso after substituting the kernel discriminant scores for the binary group labels, as well as values for the input variable observations. Empirical results are reported, showing that surrogate selection performs well.

引用

页码：241 / 258

页数：18

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