Gene selection using hybrid particle swarm optimization and genetic algorithm

Authors:
Shutao Li;Xixian Wu;Mingkui Tan
Affiliations:
Hunan University, College of Electrical and Information Engineering, 410082, Changsha, China;Hunan University, College of Electrical and Information Engineering, 410082, Changsha, China;Hunan University, College of Electrical and Information Engineering, 410082, Changsha, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2008

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Abstract

Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.