A new gene selection method for microarray data based on PSO and informativeness metric

Authors:
Jian Guan;Fei Han;Shanxiu Yang
Affiliations:
School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China;School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China;School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

Citing 9
Cited 0

Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF

Applied Intelligence
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Redundancy based feature selection for microarray data

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A review of feature selection techniques in bioinformatics

Bioinformatics
Defining an informativeness metric for clustering gene expression data

Bioinformatics
A hybrid GA/SVM approach for gene selection and classification of microarray data

EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing

Quantified Score

Hi-index	0.00

Visualization

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.