Multi-label feature selection has become an indispensable pre-processing step to deal with possible irrelevant and redundant features, to decrease computational burdens, improve classification performance and enhance model interpretability, in multi-label learning. Mutual information (MI) between two random variables is widely used to describe feature-label relevance and feature-feature redundancy. Furthermore, multivariate mutual information (MMI) is approximated via limiting three-degree interactions to speed up its computation, and then is used to characterize relevance between selected feature subset and label subset. In this paper, we combine MMI-based relevance with MI-based redundancy to define a new max-relevance and min-redundancy feature selection criterion (simply MMI). To search for a globally optimal solution, we add an auxiliary mutation operation to existing binary particle swarm optimization with mutation to control the number of selected features strictly to form a new PSO variant: M2BPSO. Integrating MMI with M2BPSO builds a novel multi-label feature selection method: MMI-PSO. The experiments on four benchmark data sets demonstrate the effectiveness of our proposed algorithm, according to four instance-based classification evaluation metrics, compared with three state-of-the-art feature selection approaches.
CITATION STYLE
Wang, X., Zhao, L., & Xu, J. (2018). Multi-label feature selection method based on multivariate mutual information and particle swarm optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11304 LNCS, pp. 84–95). Springer Verlag. https://doi.org/10.1007/978-3-030-04212-7_8
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