计算机应用研究2024，Vol.41Issue(11)：3317-3322,6.DOI:10.19734/j.issn.1001-3695.2024.04.0102
融合差分教学优化的粗糙集属性约简算法
Rough set attribute reduction algorithm based on differential teaching-learning optimization
摘要
Abstract
To address the challenges of high computational complexity and the tendency to get stuck in local optima during at-tribute reduction within traditional rough set theory,this paper proposed an innovative rough set attribute reduction algorithm based on differential teaching-learning optimization(AR-DTLBO).Leveraging the global search capabilities of the differential teaching-learning optimization algorithm along with the strengths of rough set theory in handling imprecise and uncertain data,the algorithm aimed to optimize the process.Firstly,it enhanced the teaching-learning optimization algorithm by introducing an adaptive teaching factor and a differential mutation strategy,thereby enhancing its search capabilities and optimization perfor-mance.Subsequently,it refined the attribute reduction process through the improved teaching-learning optimization algorithm's"teaching"and"learning"phases,effectively reducing the dimensionality and complexity of datasets.Finally,it conducted comparative experiments between the proposed AR-DTLBO algorithm and six other algorithms,using eight datasets from the UCI database.The experimental results demonstrate that the proposed algorithm achieves favorable outcomes in terms of reduc-tion length,reduction time,reduction rate,and classification accuracy.This successful reduction and optimization of datasets not only reduces redundant information but also enhances the precision of decision rules.These findings provide valuable sup-port for decision analysis,data mining,and other related fields.关键词
教学优化算法/教学阶段/学习阶段/差分变异策略/属性约简Key words
teaching optimization algorithm/teaching stage/learning stage/differential mutation strategy/attribute reduction分类
信息技术与安全科学引用本文复制引用
周婉婷,郑颖春,魏博涛..融合差分教学优化的粗糙集属性约简算法[J].计算机应用研究,2024,41(11):3317-3322,6.基金项目
国家自然科学基金资助项目(12001420) （12001420）
