Robust feature selection by weighted fisher criterion for multiclass prediction in gene expression profiling

Jianhua Xuan, Yibin Dong, Javed Khan, Eric Hoffman, Robert Clarke, Yue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

This paper presents a robust feature selection approach for multiclass prediction with application to microarray studies. First, individually discriminatory genes (IDGs) are identified by using weighted Fisher Criterion (wFC). Second, jointly discriminatory genes (JDGs) are selected by a sequential search method, according to their joint class separability. To combat the small size effect on feature selection, leave-one-out procedures are incorporated into both IDG and JDG selection steps to improve the robustness of the approach. By applying this approach to a microarray study of small round blue cell tumors (SRBCTs) of childhood, we have demonstrated that our robust feature selection method can be used to successfully identify a subset of genes with superior classification performance for multiclass prediction.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages291-294
Number of pages4
DOIs
StatePublished - 2004
Externally publishedYes
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004Aug 26 2004

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
CountryUnited Kingdom
CityCambridge
Period8/23/048/26/04

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