混合层次依赖度下的邻域粗糙集多目标特征选择算法  

Multi-objective Feature Selection Algorithm for Neighborhood Rough Set Under Mixed Hierarchical Dependence

作　　者：骆公志 张尚蕾 LUO Gongzhi;ZHANG Shangei(School of Management,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学管理学院,南京210003

出　　处：《数据采集与处理》2025年第1期117-133,共17页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(72171124);江苏高校哲学社会科学研究重大项目(2021SJZDA129);江苏省研究生科研创新计划项目(KYCX22_0884)。

摘　　要：精度和效率是评判特征选择算法性能的关键指标,分别对应邻域粗糙集的属性依赖度和约简规模,而已有的特征选择算法通常以属性约简的最大依赖度为导向进行寻优,忽略了约简规模的重要性。现实中,随着数据特征维度的增加和类别层次结构的出现,导致类别信息复杂且结构关系混乱,传统属性依赖度计算未有效利用类别层次结构信息,使得分类性能不佳。鉴于此,本文构造了一种综合考量属性重要度和类别层次结构关系的混合层次依赖度,将混合层次依赖度和约简规模作为两个独立的优化目标,引入多目标进化算法对其分别进行优化,从属性依赖度和属性规模两方面提升所得属性约简的性能,以得到满足目标约束的约简结果。数据实验分析结果表明,所提算法能够在目标约束内得到更高质量的约简结果,并且能够提高分类精度。Accuracy and efficiency are the key metrics for evaluating the performance of feature selection algorithms.They correspond to the attribute dependence and reduction scale of neighborhood rough sets respectively.Conventional feature selection algorithms often optimize solely based on maximum attribute dependence reduction,overlooking the significance of reduction scale.However,as data feature dimensions increase and category hierarchies emerge,category information becomes complex and structural relationships become chaotic.Traditional attribute dependency calculations fail to effectively utilize category hierarchy information,leading to suboptimal classification performance.In response to this,a mixed hierarchical dependency that considers the relationship between attribute importance and category hierarchy structure is constructed.This treats mixed hierarchical dependency and reduction scale as two independent optimization objectives,and introduces a multi-objective evolutionary algorithm to optimize them independently.This approach improves attribute reduction performance from both the attribute dependency and attribute scale perspectives,resulting in reduction results that meet target constraints.Experimental results demonstrate that the proposed algorithm achieves higher-quality reduction results within target constraints,leading to the improvement of classification accuracy.

关 键 词：多目标特征选择 邻域粗糙集 层次结构 混合层次依赖度 属性约简 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]