Journal of South China University of Technology (Natural Science Edition) ›› 2014, Vol. 42 ›› Issue (5): 143-148.doi: 10.3969/j.issn.1000-565X.2014.05.022

• Computer Science & Technology • Previous Articles    

Improved LEM2 Rule Extraction Algorithm Based on Generalized Decision Function

Ji Xia Li Long- shu Xu Yi   

  1. Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education,Anhui University,Hefei 230039,Anhui,China∥ School of Computer Science and Technology,Anhui University,Hefei 230601,Anhui,China
  • Received:2013-03-11 Revised:2014-02-08 Online:2014-05-25 Published:2014-04-01
  • Contact: 纪霞(1982-)，女，博士，讲师，主要从事不精确信息处理、粗糙集理论研究． E-mail:ahuivy1983@sina.com
  • About author:纪霞(1982-)，女，博士，讲师，主要从事不精确信息处理、粗糙集理论研究．
  • Supported by:

    安徽省自然科学基金资助项目(1308085QF114);安徽省高等学校自然科学基金资助项目(KJ2012Z020，KJ20133A015);安徽大学博士科研启动基金资助项目(33190081)

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Abstract:

In order to improve the efficiency and quality of rule extraction in LEM2 series algorithms,an improvedLEM2 algorithm on the basis of generalized decision function,namely GDF- LEM2,is proposed.In this algorithm,candidate attribute- value pair set T(G) is calculated according to generalized decision function and is downsized bydeleting newly- defined redundancy attribute- value pair sets,and thus the efficiency of rule extraction is improved.Moreover,the choice of attribute- value pair sets is guided with the minimum intersection of generalized decisionfunction strategy,which makes the extracted rule more laconic and thus improves the quality of rule extraction.Ex-perimental results show that GDF- LEM2 algorithm effectively improves the efficiency and quality of rule extractionfrom complete or incomplete decision systems.

Key words: rough set, rule extraction, LEM2 algorithm, generalized decision function