使用模糊β覆盖和模糊互信息的混合数据蚁群优化属性缩减算法

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2024-10-29 DOI:10.1016/j.asoc.2024.112373

Yuan Chen , Xiaopeng Cai , Zhaowen Li

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引用次数: 0

摘要

作为处理数据不确定性和模糊性的有效工具，模糊 β 覆盖能很好地适应给定的数据集。蚁群智能算法适用于解决复杂的组合优化问题，在属性还原方面具有独特的优势。本文提出了一种基于模糊β覆盖和模糊互信息的蚁群优化属性还原算法。首先，基于模糊β覆盖理论建立了混合数据的模糊β覆盖决策信息系统。然后，引入模糊互信息来衡量该系统的不确定性。随后，利用模糊互信息构建了一个评价函数，用于设计基于启发式搜索策略的前向属性缩减算法。此外，为了识别更多潜在的最优属性子集，还设计了一种基于随机搜索策略的蚁群优化属性缩减算法。最后，将提出的两种算法与现有的六种属性缩减算法进行了实验比较。结果表明，这两种算法在分类准确率和缩减率方面都超过了其他六种算法。

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An ant colony optimization attribute reduction algorithm for hybrid data using fuzzy β covering and fuzzy mutual information

As an effective tool for handling the uncertainty and fuzziness of data, fuzzy

β

covering can fit the given dataset well. Swarm intelligence algorithms are suitable for solving complex combinatorial optimization problems and then have unique advantages in attribute reduction. This paper proposes an ant colony optimization attribute reduction algorithm based on fuzzy

β

covering and fuzzy mutual information. Initially, a fuzzy

β

covering decision information system for hybrid data is built based on fuzzy

β

covering theory. Then, fuzzy mutual information is introduced to measure the uncertainty of this system. Subsequently, an evaluation function is constructed using fuzzy mutual information for designing a forward attribute reduction algorithm based on heuristic search strategy. Moreover, to identify potentially more optimal attribute subsets, an ant colony optimization attribute reduction algorithm based on random search strategy is designed. Finally, two proposed algorithms are experimentally compared with six existing attribute reduction algorithms. The results indicate that these two algorithms surpass the other six algorithms in terms of classification accuracy and reduction rate.

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.