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广义多粒度粗糙集特征选择算法研究
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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Abstract:

随着信息技术的迅猛发展,产生了大量的数据,这些数据体量巨大、形式多样、产生迅速、价值密度低、商业价值高。如何使这些数据对人类社会的进步产生积极影响是一个难题。粗糙集理论可以直接对数据进行降维处理,发现数据中的隐含知识,促进社会进步。经典粗糙集理论基于单个二元关系,缺乏灵活性和普遍性,基于多个二元关系的粗糙集理论可以解决上述难题,因此,本文主要针对广义多粒度粗糙集进行了研究,引入元启发式算法,提出通过元启发式算法(蚁群算法)实现广义多粒度粗糙集特征选择算法。通过实验结果看出本文所提算法可以对数据集起到降维效果且得到的特征子集的分类精度和原数据集基本保持一致。
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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