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Feature Selection Based on Fuzzy Conditional Distinction Degree

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11304))

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Abstract

Previous studies have shown that information entropy and its variants are useful at reducing data dimensionality. Yet, most existing approaches based on entropy exploit the correlations between features and labels, lacking of taking into account the relevance between features. In this paper, we propose a new index for feature selection, named fuzzy conditional distinction degree (FDD), based on fuzzy similarity relation by combining feature correlations with the relationship between features and labels. Different from existing approaches based on entropy, FDD considers the cardinality of the relation matrix instead of the similarity classes. Meanwhile, we encode the feature correlations into distance to measure the relevance of any two features. Some useful properties are discussed. Based on the FDD, a greedy forward algorithm for feature selection is presented. Experimental results on benchmark data sets denote the feasibility and effectiveness of the proposed approach.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets.html.

  2. 2.

    http://www.ntu.edu.sg/home/elhchen/data.htm.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Nos. 61473259, 61502335, 61070074, 60703038) and the Hunan Provincial Science and Technology Project Foundation (2018TP1018, 2018RS3065).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China

    Qilai Zhang

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China

    Jianhua Dai

Authors
  1. Qilai Zhang
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  2. Jianhua Dai
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Corresponding author

Correspondence to Jianhua Dai .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Zhang, Q., Dai, J. (2018). Feature Selection Based on Fuzzy Conditional Distinction Degree. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-04212-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04211-0

  • Online ISBN: 978-3-030-04212-7

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