Feature Selection of High Dimensional Data by Adaptive Potential Particle Swarm Optimization

For high-dimensional data classification, feature selection has played a significant role in removing the redundant or irrelevant features and improving the performances of classifiers. Particle Swarm Optimization (PSO), an evolutionary computational tool, has been widely and successfully applied in the field of feature selection. A recent pioneer work, Potential PSO (PPSO) which utilizes feature discretization process based on cut-points, provides better overall performance in classification and lower amount of selected features compared with some existing approaches. In this paper, we proposed an adaptive PPSO (APPSO) to reduce the number of features further and increase the classification accuracy. In APPSO, the pre-filtering method, ReliefF, is incorporated into the discretization process to help screen the potential cut-points effectively, and a new updating strategy is proposed to strengthen the searching scope of particles and avoid being trapped into local optima. Also, APPSO follows an adaptive way to alter the random level of the selection of cutpoints. The random level depends on the mean instance number per class of each dataset. Experiments on 10 datasets are carried out, and APPSO is proven to select less than 2% of the number of features while maintaining a very competitive accuracy compared with some state-of-the-art methods.