Variable Selection for Kernel Classifiers: A Feature-to-Input Space Approach

被引：0

作者：

Oosthuizen, Surette ^{[1
]}

Steel, Sarel ^{[1
]}

机构：

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

来源：

ADVANCES IN DATA ANALYSIS, DATA HANDLING AND BUSINESS INTELLIGENCE | 2010年

关键词：

Binary classification; Feature space; Kernel classifiers; Variable selection; SUPPORT VECTOR MACHINES;

D O I：

10.1007/978-3-642-01044-6_14

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An aspect of kernel classifiers which complicates variable selection is the implicit use of the transformation function Phi. This function maps the space in which the data cases reside, the so-called input space (X), to a higher dimensional feature space (T). Variable selection in F is a difficult problem, while variable selection in X is mostly inadequate. We propose an intermediate kernel variable selection approach which is implemented in X while also accounting for the fact that kernel classifiers operate in F.

引用

页码：157 / 166

页数：10

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