A Subset Similarity Guided Method for Multi objective Feature Selection
This paper presents a particle swarm optimisation PSO based multi objective feature selection method for evolving a set of non dominated feature subsets and achieving high classification performance. Firstly, a multi objective PSO named MOPSO SRD algorithm, is applied to solve feature selection problems. The results of this algorithm are then used to compare with the proposed multi objective PSO algorithm, called MOPSO SiD. MOPSO SiD is specifically designed for feature selection problems, in which a subset similarity distance measure distance in the solution space is used to select a leader for each particle in the swarm. This distance measure is also used to update the archive set, which will be the final solutions returned by the MOPSO SiD algorithm. The results show that both algorithms successfully evolve a set of non dominated solutions, which include a small number of features while achieving similar or better performance than using all features. In addition, in most case MOPSO SiD selects smaller feature subsets than MOPSO SRD, and outperforms single objective PSO for feature selection and a traditional feature selection method.
ACALCI