计算机与现代化Issue(7)：56-58,3.DOI:10.3969/j.issn.1006-2475.2013.07.014
基于Pawlak的决策粗糙集的属性约简研究
Researh on Attribute Reduction of Decision-theoretic Rough Set Model Based on Pawlak
摘要
Abstract
Rough set theory can be applied to rule induction.There are two different types of classification rules,positive and boundary rules,which leading to different decisions and consequences.They can be distinguished from the syntax measures and semantic measures.Both the two can be interpreted by a probabilistic extension of the Pawlak rough set model.Attribute reduction is an important concept of rough set theory.This paper addresses attribute reduction in decision-theoretic rough set models regarding the classification properties of decision-monotonicity and provides a positive-based Veduction model in attribute reduction and its analysis.关键词
决策粗糙集/属性约简/损失函数Key words
decision-theoretic rough set/attribute reduction/loss function分类
信息技术与安全科学引用本文复制引用
韩丽丽,李龙澍..基于Pawlak的决策粗糙集的属性约简研究[J].计算机与现代化,2013,(7):56-58,3.基金项目
安徽高等学校省级自然科学基金资助项目(KJ2011Z020) （KJ2011Z020）
