NDLI: Orthogonal forward selection and backward elimination algorithms for feature subset selection

Content Provider	IEEE Xplore Digital Library
Author	Mao, K.Z.
Copyright Year	1996
Abstract	Sequential forward selection (SFS) and sequential backward elimination (SBE) are two commonly used search methods in feature subset selection. In the present study, we derive an orthogonal forward selection (OFS) and an orthogonal backward elimination (OBE) algorithms for feature subset selection by incorporating Gram-Schmidt and Givens orthogonal transforms into forward selection and backward elimination procedures, respectively. The basic idea of the orthogonal feature subset selection algorithms is to find an orthogonal space in which to express features and to perform feature subset selection. After selection, the physically meaningless features in the orthogonal space are linked back to the same number of input variables in the original measurement space. The strength of employing orthogonal transforms is that features are decorrelated in the orthogonal space, hence individual features can be evaluated and selected independently. The effectiveness of our algorithms to deal with real world problems is finally demonstrated.
Starting Page	629
Ending Page	634
Page Count	6
File Size	191094
File Format	PDF
ISSN	10834419
Volume Number	34
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-02-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Search methods Filters Decorrelation Pattern classification Input variables Data acquisition Classification algorithms Genetic algorithms
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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