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Conditional mutual information-based feature selection algorithm for maximal relevance minimal redundancy

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Abstract

There are many feature selection algorithms based on mutual information and three-dimensional mutual information (TDMI) among features and the class label, since these algorithms do not consider TDMI among features, feature selection performance can be influenced. In view of the problem, this paper investigates feature selection based on TDMI among features. According to the maximal relevance minimal redundancy criterion, joint mutual information among the class label and feature set is adopted to describe relevance, and mutual information between feature set is exploited to describe redundancy. Then, joint mutual information among the class label and feature set as well as mutual information between feature set is decomposed. In the process of decomposing, TDMI among features is considered and an objective function is obtained. Finally, a feature selection algorithm based on conditional mutual information for maximal relevance minimal redundancy (CMI-MRMR) is proposed. To validate the performance, we compare CMI-MRMR with several feature selection algorithms. Experimental results show that CMI-MRMR can achieve better feature selection performance.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61771334).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Xiangyuan Gu, Jichang Guo, Lijun Xiao & Chongyi Li

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  1. Xiangyuan Gu
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  2. Jichang Guo
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  3. Lijun Xiao
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  4. Chongyi Li
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Correspondence to Jichang Guo.

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Gu, X., Guo, J., Xiao, L. et al. Conditional mutual information-based feature selection algorithm for maximal relevance minimal redundancy. Appl Intell 52, 1436–1447 (2022). https://doi.org/10.1007/s10489-021-02412-4

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