Abstract
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.
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Nguyen, H.B., Xue, B., Zhang, M. (2016). A Subset Similarity Guided Method for Multi-objective Feature Selection. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_25
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DOI: https://doi.org/10.1007/978-3-319-28270-1_25
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