Particle Swarm Optimization for Feature Selection : A Study on Microarray Data Classification

DNA microarray technology allows simultaneous monitoring and measuring of thousands of gene expression activation levels in a single experiment. Data mining techniques such as classification is widely used on microarray data for medical diagnosis and gene analysis. However, high dimensionality of the data affects the performance of classification and prediction. Consequently, a key issue in microarray data is feature selection and dimensionality reduction in order to achieve better classification and predictive accuracy. There are several machine learning approaches available for feature selection. In this study, particle swarm optimization technique was used for feature selection, and the classification performance of several popular classifiers was analyzed on a set of microarray datasets. The results conclude that particle swarm optimization technique provides better results compared to genetic algorithm