A Novel Approach to Select Important Genes from Microarray Data

摘要：

Feature subset selection is a well-known pattern recognition problem, which aims to reduce the number of features used in classi.cation or recognition. This reduction is expected to improve the performance of classi.cation 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 classi.er. We evaluate the performance of Ascore 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 signi.cantly reduce the number of genes, and perform better than T-score and C-score.

关键词： Microarray data Feature selection Important genes A-score

作者: Xianchang Wang Lishi Zhang Junfu Du

作者单位: School of Science,Dalian Ocean University,116023,Dalian,China

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 3497-3500

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）