基于论域离散度的属性约简算法

doi:10.6040/j.issn.1671-9352.4.2022.1581

摘要/Abstract

摘要： 提出了一种基于论域离散度的适应度函数,在前向贪心搜索策略下,以该适应度函数评估条件属性的重要性,进而求取邻域粗糙集的约简。该算法与3个比较流行的属性约简算法进行对比实验,在12组UCI数据集上进行验证。实验结果表明,与另外3种算法相比,在不降低分类效果的情况下,本文算法在时间消耗和稳定性上具有较为明显的优势。

关键词: 属性约简, 条件属性, 适应度函数, 邻域粗糙集

Abstract: A fitness function based on the dispersion of the universe was proposed. Under the forward greedy searching strategy, the fitness function was employed to evaluate the importance of conditional attributes and then used to derive the reduct of the neighborhood rough set. The proposed algorithm was compared with three state-of-the-art attribute reduction algorithms and validated on 12 UCI datasets. Experimental results showed that compared with the other three algorithms, the proposed algorithm exhibited better in time consumption and stability without reducing the classification effect.

Key words: attribute reduction, conditional attribute, fitness function, neighborhood rough set

中图分类号:

TP181

刘长顺,刘炎,宋晶晶,徐泰华. 基于论域离散度的属性约简算法[J]. 《山东大学学报(理学版)》, 2023, 58(5): 26-35.

LIU Changshun, LIU Yan, SONG Jingjing, XU Taihua. Attribute reduction algorithm based on discreteness of the universe[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(5): 26-35.

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