Improved PSO for Feature Selection on High-Dimensional Datasets

Classification on high-dimensional (i.e. thousands of dimensions) data typically requires feature selection (FS) as a pre-processing step to reduce the dimensionality. However, FS is a challenging task even on datasets with hundreds of features. This paper proposes a new particle swarm optimisation (PSO) based FS approach to classification problems with thousands or tens of thousands of features. The proposed algorithm is examined and compared with three other PSO based methods on five high-dimensional problems of varying difficulty. The results show that the proposed algorithm can successfully select a much smaller number of features and significantly increase the classification accuracy over using all features. The proposed algorithm outperforms the other three PSO methods in terms of both the classification performance and the number of features. Meanwhile, the proposed algorithm is computationally more efficient than the other three PSO methods because it selects a smaller number of features and employs a new fitness evaluation strategy.