Classification Rule Mining Algorithm Combining Intuitionistic Fuzzy Rough Sets and Genetic Algorithm


This paper has proposed a classification rule base mining algorithm combining the genetic algorithm and intuitionistic fuzzy-rough set for large-scale intuitionistic fuzzy information system. The algorithm has proposed innovatively the definitions and measurement metrics of completeness, interaction and compatibility describing the whole rule base, and constructed a multi-objective optimization model to optimize the population size of data sample, and used the intuitionistic fuzzy-rough set to reduce the attribute set of fuzzy information system, and used intuitionistic fuzzy similar class to extract large-scale intuitionistic fuzzy information system rules, and obtained an optimal rule base with the minimal size, configuration, generation time and storage space. A threshold control mechanism is used to evaluate the completeness, interaction and correlation of rule population and improves the robustness and flexibility of sample population optimization and rule base generation. The algorithm is verified by the real aircraft health data sets. The algorithm is validated by similar mature and effective algorithms in accuracy, time complexity using real data and has good robustness and adaptability to different size large-scale fuzzy information system.

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This article was funded by the China Aviation Science Foundation (20150267001), and the author is very grateful. The author wishes to thank those researchers such as Lei Yingjie, Kong Weiwei, who had studied relevant theories and algorithms of completeness, interaction and compatibility for intuitionistic fuzzy rough classification rules.

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Correspondence to Chuanchao Zhang.

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Zhang, C. Classification Rule Mining Algorithm Combining Intuitionistic Fuzzy Rough Sets and Genetic Algorithm. Int. J. Fuzzy Syst. 22, 1694–1715 (2020).

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  • Large-scale intuitionistic fuzzy information system
  • Intuitionistic fuzzy classification rule base
  • Completeness
  • Interaction
  • Compatibility
  • Multi-objective optimization
  • Genetic algorithm