数据采集与处理2025，Vol.40Issue(3)：807-820,14.DOI:10.16337/j.1004-9037.2025.03.019
基于决策代价融合度量的不完备邻域决策粗糙集属性约简
Attribute Reduction of Incomplete Neighborhood Decision Rough Sets Based on Decision-Cost Fusion Measures
摘要
Abstract
Attribute reduction relies on knowledge granulation and uncertainty measurement,thus facilitating intelligent recognition.For incomplete continuous data,neighborhood decision rough sets induce attribute reduction.However,the related neighborhood relation deserves optimal improvements,while the existing decision cost deserves integrated reinforcements.In this paper,a new neighborhood relation is proposed,and three decision-cost fusion measures are constructed,so new incomplete neighborhood decision rough sets are established and the attribute reduction is systematically researched.At first,an improved distance is introduced to produce an incomplete neighborhood relation,so improved rough sets on incomplete neighborhood are proposed.Then,the dependence degree and neighborhood entropy are introduced based on decision costs,so three fusion measures on decision costs are obtained by multiplication fusion,thus acquiring granulation non-monotonicity.Furthermore,eight heuristic reduction algorithms based on attribute importances are designed from two neighborhood relations and four relevant measures of decision costs.As finally verified by data experiments,the five algorithms out of the seven new algorithms have good performance of classification learning,thus improving the basic reduction algorithm.关键词
属性约简/粗糙集/不完备邻域关系/不确定性度量/决策代价Key words
attribute reduction/rough set/incomplete neighborhood relation/uncertainty measure/decision cost分类
信息技术与安全科学引用本文复制引用
张万祥,张贤勇,杨霁琳,陈本卫..基于决策代价融合度量的不完备邻域决策粗糙集属性约简[J].数据采集与处理,2025,40(3):807-820,14.基金项目
国家自然科学基金(61976158) （61976158）
四川省自然科学基金(2024NSFSC0486,2024NSFSC0443) （2024NSFSC0486,2024NSFSC0443）
四川省科技计划(2022ZYD0001) （2022ZYD0001）
教育部人文社科规划基金(23YJA630114). （23YJA630114）
