Paper Title:
Efficient Feature Selection Model for Gene Expression Data
  Abstract

Finding subset of informative gene is very crucial for biology process because several genes increase sharply and most of them are not related with others. In general, feature selection technique consists of two steps 1) all genes is ranked by a filter approach 2) rank list is sent to a wrapper approach. Nevertheless, the accuracy rate for recognition gene is not enough. Therefore, this paper proposes efficient feature selection model for gene expression data. First, two filter approaches are used to define many subset of attribute such as Correlation based Feature Selection (Cfs) and Gain Ratio (GR). Second, wrapper approach is used to evaluate each length of attribute that based on Support Vector Machine (SVM) and Random Forest (RF). The result of experiment depicts CfsSVM, CfsRF, GRSVM, and GRRF based on proposed model produce higher accuracy rate such as 87.10%, 90.32%, 87.10, and 88.71%, respectively.

  Info
Periodical
Chapter
Chapter 10: Biomedical Manufacturing
Edited by
Wu Fan
Pages
1948-1952
DOI
10.4028/www.scientific.net/AMM.110-116.1948
Citation
P. Saengsiri, S. N. Wichian, P. Meesad, "Efficient Feature Selection Model for Gene Expression Data", Applied Mechanics and Materials, Vols. 110-116, pp. 1948-1952, 2012
Online since
October 2011
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Price
$32.00
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