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Design of fuzzy hyperbox classifiers based on a two-stage genetic algorithm and simultaneous strategy

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Abstract

The fuzzy min-max (FMM) neural network can be regarded as a typical fuzzy hyperbox classifier that is designed in a sequential way, which leads to an input order drawback and overlap elimination limitation. In this paper, we propose a two-stage-based genetic algorithm (TGA) to construct blue a fuzzy hyperbox classifier (FHC) in a simultaneous way. The simultaneous method is realized by estimating all parameters of hyperboxes at one time rather than by separately determining the parameters of hyperboxes in a sequential way. In this paper, we propose a two-stage-based genetic algorithm to construct the fuzzy hyperbox classifier. The overall TGA consists of two stages, namely, the construction stage and optimization stage. The construction stage is aimed at designing the FHC structure, while the goal of the optimization stage is to further optimize the FHC structure. Using a two-stage genetic algorithm to directly construct a fuzzy hyperbox classifier can overcome the problem of input order and hyperbox overlap. The experimental results show that the proposed FHC yields higher classification accuracy in comparison with the stage-of-the-art FMMs reported in the literature.

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Data availability and access

The data that support the findings of this study are openly available in [UCI] at [https://archive.ics.uci.edu/ml/index.php]

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Funding

This work was supported by National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China (Grant No. 62227805) and by the Open Foundation of State Key Laboratory of Complex Electronic System Simulation, Beijing, China (Grant No. 614201001032104)

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Authors and Affiliations

  1. School of Cyberspace Science and Technology, Beijing Institute of Technology, 100081, Beijing, China

    Wei Huang

  2. School of Computer Science and Engineering, Tianjin University of Technology, 300384, Tianjin, China

    Mengyu Duan

  3. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, 518110, Shenzhen, China

    Shaohua Wan

Authors
  1. Wei Huang
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  2. Mengyu Duan
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  3. Shaohua Wan
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Contributions

Mengyu Duan: Conceptualization, Data Curation, Software, Visualization, Validation, Writing - original draft.Wei Huang: Methodology, Formal analysis, Funding acquisition, Supervision, Writing - review & editing. Shaohua Wan: Funding acquisition, Investigation, Project.

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Correspondence to Wei Huang.

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Huang, W., Duan, M. & Wan, S. Design of fuzzy hyperbox classifiers based on a two-stage genetic algorithm and simultaneous strategy. Appl Intell 54, 1426–1444 (2024). https://doi.org/10.1007/s10489-023-04986-7

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