经典的知识依赖性及属性重要性度量的新注记

计算机科学 ›› 2016, Vol. 43 ›› Issue (2): 273-276.doi: 10.11896/j.issn.1002-137X.2016.02.057

经典的知识依赖性及属性重要性度量的新注记

陈飞,姜麟,李金海

昆明理工大学理学院昆明650500,昆明理工大学理学院昆明650500,昆明理工大学理学院昆明650500

出版日期:2018-12-01 发布日期:2018-12-01
基金资助:
本文受国家自然科学基金(61305057),云南省教育厅基金(2010Y389)资助

New Note on Classical Measure of Knowledge Dependency and Attribute Significance

CHEN Fei, JIANG Lin and LI Jin-hai

Online:2018-12-01 Published:2018-12-01

摘要/Abstract

摘要： 知识依赖性及属性重要性度量是粗糙集的重要概念,广泛应用于知识约简和规则提取等方面。经典的知识依赖性及属性重要性度量在处理数据方面有局限性,有时无法得到较为精确、合理的度量结果,从而导致后续应用中得到的结果出现一系列的偏差。因此,通过深度分析经典知识依赖性,结合多数包含关系,并加入可信系数,提出了一种新的知识依赖性及属性重要性度量方法。最后,将新度量方法应用于一个决策信息系统,分析结果表明新度量方法是有效的。

关键词: 粗糙集,决策信息系统,知识依赖性,属性重要性,度量

Abstract: Measure of knowledge dependency and attribute significance is an important issue in rough set theory.It has been widely applied to knowledge reduction,rule extraction,etc.Classical measure of knowledge dependency and attri-bute significance has a littie bit of limitation in dealing with data,and sometimes it cannot obtain precise and reasonable results,which leads to a series of deviations in the subsequent applications.To this end,through deep analysis of the exi-sting classical knowledge dependency,combining it with the majority inclusion relation,and adding credibility parameters,a new method of measuring knowledge dependency and attribute significance was proposed.Finally,the new mea-sure method was applied to decision information system.And the analysis results show that the new method is effective.

Key words: Rough set,Decision information system,Knowledge dependency,Attribute significance,Measure

陈飞,姜麟,李金海. 经典的知识依赖性及属性重要性度量的新注记[J]. 计算机科学, 2016, 43(2): 273-276. https://doi.org/10.11896/j.issn.1002-137X.2016.02.057

CHEN Fei, JIANG Lin and LI Jin-hai. New Note on Classical Measure of Knowledge Dependency and Attribute Significance[J]. Computer Science, 2016, 43(2): 273-276. https://doi.org/10.11896/j.issn.1002-137X.2016.02.057

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