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Researches on Feature Selection Algorithm for Generalized Multi-Granularity Rough Sets

DOI: 10.12677/HJDM.2023.133021, PP. 213-221

Keywords: 粒计算,特征选择,广义多粒度粗糙集,二元关系,Granular Computing, Feature Selection, Generalized Multi-Granularity Rough Sets, Binary Relationships

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With the rapid development of information technology, a large amount of data has been generated, which is huge in volume, diverse in form, rapid in generation, low in value density, and high in commercial value. How to make these data have a positive impact on the progress of human society is a challenge. Rough set theory can directly reduce the dimensionality of the data, discover the implicit knowledge in the data, and promote the social progress. The classical rough set theory is based on a single binary relationship, which lacks flexibility and universality. The rough set theory based on multiple binary relationships can solve the above problems. Therefore, this paper mainly focuses on the generalized multi-granularity rough set and introduces the meta-heuristic algorithm, and proposes to implement the generalized multi-granularity rough set feature selection algorithm by the meta-heuristic algorithm (ant colony algorithm). The experimental results show that the proposed algorithm can reduce the dimensionality of the data set and the classification accuracy of the obtained feature subsets is basically consistent with the original data set.


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