Fast orthogonal forward selection algorithm for feature subset selection

被引：70

作者：

Mao, KZ ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 05期

关键词：

feature selection; forward selection; orthogonal decomposition;

D O I：

10.1109/TNN.2002.1031954

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection is an important issue in pattern classification. In the present study, we develop a fast orthogonal forward selection (FOFS) algorithm for feature subset selection. The FOFS algorithm employs orthogonal transform to decompose correlations among candidate features, but it performs the orthogonal decomposition in an implicit way. Consequently, the fast algorithm demands less computational efforts as compared with the conventional orthogonal forward selection (OFS).

引用

页码：1218 / 1224

页数：7

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