文档介绍：八十三週年校慶基礎學術研討會民國九十六年六月一日
A Novel Approach for Fuzzy Rule Extraction based on
Rough Set Theory and Entropy
a a, b
Tien-Chin Wang Hsien-Da Lee
a
Department of Information Management, I-Shou University, Kaohsiung 840, Taiwan
******@
b
Department of Information Management, Fortune Institute of Technology, Kaohsiung,
Taiwan
******@.tw
Abstract
Rule extraction is an important theme in data mining. Fuzzy set theory(FST) and Rough
set theory(RST) are mon technologies frequently applied to data mining tasks.
Decision induction is one mon 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.
Keywords:Rule Extraction, Fuzzy Set Theory, Rough Set Theory, Entropy, Data Minin