An attribute reduction algorithm using relative decision mutual information in fuzzy neighborhood decision system

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiucheng Xu, Shan Zhang, Miaoxian Ma, Wulin Niu, Jianghao Duan
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Abstract

The fuzzy neighborhood rough set integrates the strengths of fuzzy rough set and neighborhood rough set, serving as a pivotal extension of the rough set theory in attribute reduction. However, this model’s widespread application is hindered by its sensitivity to data distribution and limited efficacy in assessing classification uncertainty for datasets with substantial density variations. To mitigate these challenges, this paper introduces an attribute reduction algorithm based on fuzzy neighborhood relative decision mutual information. Firstly, the classification uncertainty of samples is initially defined in terms of relative distance. Simultaneously, the similarity relationship of fuzzy neighborhoods is reformulated, thereby reducing the risk of sample misclassification through integration with variable-precision fuzzy neighborhood rough approximation. Secondly, the notion of representative sample is introduced, leading to a redefinition of fuzzy membership. Thirdly, fuzzy neighborhood relative mutual information from the information view is constructed and combined with fuzzy neighborhood relative dependency from the algebraic view to propose fuzzy neighborhood relative decision mutual information. Finally, an attribute reduction algorithm is devised based on fuzzy neighborhood relative decision mutual information. This algorithm evaluates the significance of attributes by integrating both informational and algebraic perspectives. Comparative tests on 12 public datasets are conducted to assess existing attribute approximation algorithms. The experimental results show that the proposed algorithm achieved an average classification accuracy of 91.28\(\%\) with the KNN classifier and 89.86\(\%\) with the CART classifier. In both classifiers, the algorithm produced an average reduced subset size of 8.54. While significantly reducing feature redundancy, the algorithm consistently maintains a high level of classification accuracy.

Abstract Image

模糊邻域决策系统中基于相对决策互信息的属性约简算法
模糊邻域粗糙集综合了模糊粗糙集和邻域粗糙集的优点,是粗糙集理论在属性约简中的重要扩展。然而,该模型的广泛应用受到其对数据分布的敏感性和在评估具有较大密度变化的数据集的分类不确定性方面的有限功效的阻碍。为了缓解这些挑战,本文引入了一种基于模糊邻域相对决策互信息的属性约简算法。首先,用相对距离来初步定义样本的分类不确定度。同时,对模糊邻域的相似关系进行了重新表述,通过与变精度模糊邻域粗逼近的结合,降低了样本误分类的风险。其次,引入代表性样本的概念,对模糊隶属度进行了重新定义。第三,构造信息视图中的模糊邻域相对互信息,并结合代数视图中的模糊邻域相对依赖关系,提出模糊邻域相对决策互信息。最后,设计了一种基于模糊邻域相对决策互信息的属性约简算法。该算法通过整合信息和代数视角来评估属性的重要性。在12个公共数据集上进行了对比测试,以评估现有的属性近似算法。实验结果表明,该算法在KNN分类器上的平均分类精度为91.28 \(\%\),在CART分类器上的平均分类精度为89.86 \(\%\)。在这两个分类器中,该算法产生的平均减少子集大小为8.54。在显著降低特征冗余的同时,算法始终保持较高的分类准确率。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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