Title :
A novel approach to select important genes from microarray data
Author :
Wang, Xianchang ; Zhang, Lishi ; Du, Junfu
Author_Institution :
Sch. of Sci., Dalian Ocean Univ., Dalian, China
Abstract :
Feature subset selection is a well-known pattern recognition problem, which aims to reduce the number of features used in classification or recognition. This reduction is expected to improve the performance of classification algorithms in terms of speed, accuracy and simplicity. Most existing feature selection investigations are not suitable for microarray data, so this paper focuses on gene selection problem. The main contributions of this paper are that a new feature selection method A-score is introduced and constructed an improved fuzzy Bayesian classifier. We evaluate the performance of A-score using three well-known benchmark data sets: the iris data, the wine data, and the Wisconsin breast cancer data and two microarray data: ALL-AML Leukemia and colon cancer. In general, A-score can significantly reduce the number of genes, and perform better than T-score and C-score.
Keywords :
Bayes methods; cancer; feature extraction; fuzzy set theory; genetics; lab-on-a-chip; medical computing; pattern classification; A-score method; feature subset selection; fuzzy Bayesian classifier; gene selection; microarray data; pattern classification; pattern recognition problem; Accuracy; Bayesian methods; Breast; Cancer; Colon; Feature extraction; Iris; A-score; Feature selection; Important genes; Microarray data;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968721