Abstract
Feature selection (FS) aims to remove the redundant or irrelevant features of the data, which plays a very important role in data mining and machine learning tasks. Recent studies focus on integrating the data discretization technique into the FS process to help achieve superior performances in classification. In this paper, we proposed an improved discretization-based FS method via particle swarm optimization to obtain a higher classification accuracy with a smaller size of feature subset. In our approach, we use a novel encoding and decoding way for particle swarm optimization (PSO) which can efficiently select multiple cut-points for discretization. In addition, a new updating strategy and a local search procedure is proposed to strengthen the searching ability and avoid being trapped into local optimal. Experimental results on benchmark datasets demonstrate the efficacy of our proposed methods both in the classification accuracy and the feature subset size.
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Acknowledgment
This work is supported in part by the Natural Science Foundation of China under Grant 61702336, in part by Shenzhen Emerging Industries of the Strategic Basic Research Project JCYJ20170302154254147, and in part by Natural Science Foundation of SZU (grant No. 2018068).
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Lin, J., Zhou, Y., Kang, J. (2019). An Improved Discretization-Based Feature Selection via Particle Swarm Optimization. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_27
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DOI: https://doi.org/10.1007/978-3-030-29563-9_27
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