基于广义决策组合熵的属性约简

Attribute Reduction Based on Generalized Decision Combination Entropy

摘要

针对传统优势粗糙集在属性规模扩大时约简效率低、分类性能受限的问题,首先,基于广义决策类,结合属性依赖度和信息熵构造广义决策组合熵,从不同角度反应属性的重要性.其次,基于该度量对属性进行重要性排序,并通过机器学习分类器获取有较高分类精度的属性子集.最后,通过对比实验验证该方法的有效性.

Abstract

To address the low reduction efficiency and limited classification performance of traditional dominance-based rough sets when the attribute scale expands, this paper introduces a generalized decision combination entropy derived generalized decision classes by integrating attribute dependency and information entropy. This measure reflects attribute importance from multiple perspectives. Based on this measure, attributes are ranked by importance, and a machine learning classifier is used to select an attribute subset with high classification accuracy. Comparative experiments validate the effectiveness of the proposed method.

关键词

优势粗糙集 / 广义决策类 / 广义决策组合熵 / 下近似算子

Key words

dominance-based rough sets / generalized decision classes / generalized decision combination entropy / lower approximation operator

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基金资助

国家自然科学基金(12571570)

国家自然科学基金(12101074)

陕西省自然科学基金(2022JM-032)