Fuzzy Entropy-based Rough Set Approach for Extracting Decision Rules

摘要：

Rule extraction is an important theme in data mining. Fuzzy set theory (FST) and Rough set theory (RST) are two common technologies frequently applied to data mining tasks. Decision induction is one of common approaches for extracting rules in data mining. Integrating the advantages of FST and RST, this paper proposes a hybrid system to efficiently extract decision rules from a decision table. Through fuzzy sets, numeric attributes can be represented by fuzzy numbers, interval values as well as crisp values. Second, the paper proposes to utilize information gain for distinguishing importance among attributes. Then, by applying rough set approach, a decision table can be reduced by removing redundant attributes without any information loss. Finally, decision rules can be extracted from the equivalence classes. An experiment result is also presented to show the applicability of the proposed method.

关键词： Rule Extraction Fuzzy Set Theory Rough Set Theory Entropy Data Mining

作者: Tien-Chin Wang Lisa Y. Chen Hsien-Da Lee

作者单位: Department of Information Management, I-Shou University, Kaohsiung, Taiwan Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan

会议类型: 国际会议

会议名称: 第三届IEEE无线通讯、网络技术暨移动计算国际会议

会议地点: 上海

会议语种:英文

在线出版日期: 2007-09-21（万方平台首次上网日期，不代表论文的发表时间）