Title
A new gene selection method for microarray data based on PSO and informativeness metric
Abstract
In this paper, a new method encoding a priori information of informativeness metric of microarray data into particle swarm optimization (PSO) is proposed to select informative genes. The informativeness metric is an analysis of variance statistic that represents the regulation hide in the microarray data. In the new method, the informativeness metric is combined with the global searching algorithms PSO to perform gene selection. The genes selected by the new method reveal the data structure highly hided in the microarray data and therefore improve the classification accuracy rate. Experiment results on two microarray datasets achieved by the proposed method verify its effectiveness and efficiency.
Year
DOI
Venue
2013
10.1007/978-3-642-39482-9_17
ICIC (2)
Keywords
Field
DocType
new gene selection method,informative gene,experiment result,gene selection,classification accuracy rate,new method,informativeness metric,algorithms pso,data structure,microarray data,particle swarm optimization
Particle swarm optimization,Data structure,Data mining,Search algorithm,Statistic,Pattern recognition,Extreme learning machine,Computer science,A priori and a posteriori,Microarray analysis techniques,Artificial intelligence,Encoding (memory)
Conference
Citations 
PageRank 
References 
1
0.35
10
Authors
3
Name
Order
Citations
PageRank
GUAN Jian14715.77
Fei Han224126.37
Shanxiu Yang350.78