A PSO Based Hybrid Feature Selection Algorithm For High-Dimensional Classification

Recent research has shown that Particle Swarm Optimisation is a promising approach to feature selection. However, applying it on high-dimensional data with thousands to tens of thousands of features is still challenging because of the large search space. While filter approaches are time efficient and scalable for high-dimensional data, they usually obtain lower classification accuracy than wrapper approaches. On the other hand, wrapper methods require a longer running time than filter methods due to the learning algorithm involved in fitness evaluation. This paper proposes a new strategy of combining filter and wrapper approaches in a single evolutionary process in order to achieve smaller feature subsets with better classification performance in a shorter time. A new local search heuristic using symmetric uncertainty is proposed to refine the solutions found by PSO and a new hybrid fitness function is used to better evaluate candidate solutions. The proposed method is examined and compared with three recent PSO based methods on eight high-dimensional problems of varying difficulty. The results show that the new hybrid PSO is more effective and efficient than the other methods.© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.