Similarity based Multi-objective Particle Swarm Optimisation for Feature Selection in Classification
نویسندگان
چکیده
This paper presents a particle swarm optimisation (PSO) based multi-objective feature selection approach to evolving a set of non-dominated feature subsets and achieving high classification performance. Firstly, a pure multi-objective PSO (named MOPSO-SRD) algorithm, is applied to solve feature selection problems. The results of this algorithm is then used to compare with the proposed a multi-objective PSO algorithm, called MOPSOSiD. MOPSO-SiD is specially designed for feature selection problems, in which the similarity distance in the feature 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 solution for a MOPSO algorithm. The results show that both algorithms successfully evolve a set of non-dominated solutions, which select a small number of features while achieving similar or better performance than using all features. In addition, in most case MOPSO-SiD selects smaller number of features than MOPSO-SRD, and outperforms single objective PSO for feature selection and two traditional feature selection methods.
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