An Interpretable Feature Selection Based on Particle Swarm Optimization

Gensong LI
Mengmeng LI

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.8    pp.1495-1500
Publication Date: 2022/08/01
Publicized: 2022/05/09
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2021EDL8095
Type of Manuscript: LETTER
Category: Pattern Recognition
feature selection,  interpretable,  particle swarm optimization,  ensemble learning,  stability,  artificial intelligence,  

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Feature selection based on particle swarm optimization is often employed for promoting the performance of artificial intelligence algorithms. However, its interpretability has been lacking of concrete research. Improving the stability of the feature selection method is a way to effectively improve its interpretability. A novel feature selection approach named Interpretable Particle Swarm Optimization is developed in this paper. It uses four data perturbation ways and three filter feature selection methods to obtain stable feature subsets, and adopts Fuch map to convert them to initial particles. Besides, it employs similarity mutation strategy, which applies Tanimoto distance to choose the nearest 1/3 individuals to the previous particles to implement mutation. Eleven representative algorithms and four typical datasets are taken to make a comprehensive comparison with our proposed approach. Accuracy, F1, precision and recall rate indicators are used as classification measures, and extension of Kuncheva indicator is employed as the stability measure. Experiments show that our method has a better interpretability than the compared evolutionary algorithms. Furthermore, the results of classification measures demonstrate that the proposed approach has an excellent comprehensive classification performance.

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