基于改进扩展正域的属性核与属性约简方法

J4 ›› 2012, Vol. 47 ›› Issue (1): 72-76.

基于改进扩展正域的属性核与属性约简方法

冯林^1,2,罗芬^3,方丹³,原永乐²

1.四川师范大学可视化计算与虚拟现实四川省重点实验室, 四川成都 610068;
2.四川师范大学计算机科学学院, 四川成都 610101;
3.四川师范大学工学院, 四川成都 610101

收稿日期:2011-06-17 出版日期:2012-01-20 发布日期:2012-06-29
作者简介:冯林(1972- ),男,副教授,研究方向为粗糙集理论及应用. Email: scfengyc@126.com
基金资助:
可视化计算与虚拟现实四川省重点实验室基金资助项目(J2010N01); 四川师范大学重点研究课题基金资助

Approaches for attribute core and attribute reduction based on an improved extended positive region

FENG Lin^1,2, LUO Feng³, FANG Dan³, YUAN Yong-le¹

1. Sichuan Key Laboratory of Visualization Computing and Virtual Reality, Sichuan Normal University, Chengdu 610068,
Sichuan, China; 2. College of Computer Science, Sichuan Normal University, Chengdu 610101, Sichuan, China;
3. College of Technology, Sichuan Normal University, Chengdu 610101, Sichuan, China

Received:2011-06-17 Online:2012-01-20 Published:2012-06-29

摘要/Abstract

摘要：

指出了不相容决策表中存在的正域扩展方法的不足,基于决策表局部最小确定性与条件属性对决策的最小确定性程度,构建了一种改进的扩展正域方法。基于改进的扩展正域方法,提出了计算不相容决策表中认知属性核和认知属性约简的算法。实验结果表明了本文方法的有效性。

关键词: 粗糙集;DTRS模型;属性约简;属性核

Abstract:

The disadvantage of the existing approaches for extending the positive region from the inconsistency decision table is analyzed. Based on the self-learning model under uncertain condition, a new approach for extending the positive region is established. Finally, algorithms for calculating cognitive attribute core and cognitive attribute reduction are developed. Simulation results illustrate the efficiency of these algorithms.

Key words: rough set; decision-theoretic rough set; attribute reduction; core attribute

冯林1,2,罗芬3,方丹3,原永乐2. 基于改进扩展正域的属性核与属性约简方法[J]. J4, 2012, 47(1): 72-76.

FENG Lin1,2, LUO Feng3, FANG Dan3, YUAN Yong-le1. Approaches for attribute core and attribute reduction based on an improved extended positive region[J]. J4, 2012, 47(1): 72-76.

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