Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimization

Abstract

Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively small as in the micro-array data analysis, feature selection is even more important. This paper proposes a novel feature selection method to perform gene selection from DNA microarray data. The method originates from the least squares support vector machine (LSSVM). The particle swarm optimization (PSO) algorithm is also employed to perform optimization. Experimental results clearly demonstrate good and stable performance of the proposed method.