Multiclass classification and gene selection with a stochastic algorithm

Lê Cao, Kim-Anh, Bonnet, Agnès and Gadat, Sébastien (2009) Multiclass classification and gene selection with a stochastic algorithm. Computational Statistics and Data Analysis, 53 10: 3601-3615. doi:10.1016/j.csda.2009.02.028

Author Lê Cao, Kim-Anh
Bonnet, Agnès
Gadat, Sébastien
Title Multiclass classification and gene selection with a stochastic algorithm
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2009-08
Sub-type Article (original research)
DOI 10.1016/j.csda.2009.02.028
Volume 53
Issue 10
Start page 3601
End page 3615
Total pages 15
Place of publication Netherlands
Publisher Elsevier BV
Language eng
Subject 0104 Statistics
06 Biological Sciences
Abstract Microarray technology allows for the monitoring of thousands of gene expressions in various biological conditions, but most of these genes are irrelevant for classifying these conditions. Feature selection is consequently needed to help reduce the dimension of the variable space. Starting from the application of the stochastic meta-algorithm “Optimal Feature Weighting” (OFW) for selecting features in various classification problems, focus is made on the multiclass problem that wrapper methods rarely handle. From a computational point of view, one of the main difficulties comes from the unbalanced classes situation that is commonly encountered in microarray data. From a theoretical point of view, very few methods have been developed so far to minimize the classification error made on the minority classes. The OFW approach is developed to handle multiclass problems using CART and one-vs-one SVM classifiers. Comparisons are made with other multiclass selection algorithms such as Random Forests and the filter method F-test on five public microarray data sets with various complexities. Statistical relevancy of the gene selections is assessed by computing the performances and the stability of these different approaches and the results obtained show that the two proposed approaches are competitive and relevant to selecting genes classifying the minority classes. Application to a pig folliculogenesis study follows and a detailed interpretation of the genes that were selected shows that the OFW approach answers the biological question.
Q-Index Code C1
Q-Index Status Provisional Code

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 16 Sep 2010, 13:23:08 EST by Laura McTaggart on behalf of Institute for Molecular Bioscience