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Efficient feature selection for logical analysis of large-scale multi-class datasets

Author

Listed:
  • Kedong Yan

    (School of Computer Science and Engineering, Nanjing University of Science and Technology)

  • Dongjing Miao

    (Faculty of Computing, Harbin Institute of Technology)

  • Cui Guo

    (Business School, Shantou University)

  • Chanying Huang

    (School of Computer Science and Engineering, Nanjing University of Science and Technology)

Abstract

Feature selection in logical analysis of data (LAD) can be cast into a set covering problem. In this paper, extending the results on feature selection for binary classification using LAD, we present a mathematical model that selects a minimum set of necessary features for multi-class datasets and develop a heuristic algorithm that is both memory and time efficient for this model correspondingly. The utility of the algorithm is illustrated on a small example and the superiority of our work is demonstrated through experiments on 6 real-life multi-class datasets from UCI repository.

Suggested Citation

  • Kedong Yan & Dongjing Miao & Cui Guo & Chanying Huang, 2021. "Efficient feature selection for logical analysis of large-scale multi-class datasets," Journal of Combinatorial Optimization, Springer, vol. 42(1), pages 1-23, July.
    • Handle: RePEc:spr:jcomop:v:42:y:2021:i:1:d:10.1007_s10878-021-00732-2
    DOI: 10.1007/s10878-021-00732-2
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    References listed on IDEAS

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