Mining coverage-based fuzzy rules by evolutional computation

Author

Hong, Tzung-Pei ; Lee, Yeong-Chyi

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Kaohsiung, Taiwan

fYear

2001

fDate

2001

Firstpage

218

Lastpage

224

Abstract

The authors propose a novel mining approach based on the genetic process and an evaluation mechanism to automatically construct an effective fuzzy rule base. The proposed approach consists of three phases: fuzzy-rule generating, fuzzy-rule encoding and fuzzy-rule evolution. In the fuzzy-rule generating phase, a number of fuzzy rules are randomly generated. In the fuzzy-rule encoding phase, all the rules generated are translated into fixed-length bit strings to form an initial population. In the fuzzy-rule evolution phase, genetic operations and credit assignment are applied at the rule level. The proposed mining approach chooses good individuals in the population for mating, gradually creating better offspring fuzzy rules. A concise and compact fuzzy rule base is thus constructed effectively without human expert intervention

Keywords

data mining; fuzzy set theory; genetic algorithms; learning (artificial intelligence); very large databases; compact fuzzy rule base; coverage-based fuzzy rule mining; credit assignment; data mining; evaluation mechanism; evolutional computation; fixed-length bit strings; fuzzy set; fuzzy-rule encoding phase; fuzzy-rule evolution phase; fuzzy-rule generating phase; fuzzy-rule generation; genetic algorithm; genetic operations; genetic process; machine learning; mining approach; offspring fuzzy rules; rule level; Data mining; Encoding; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic engineering; Humans; Knowledge acquisition; Machine learning; Random number generation;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on

Conference_Location

San Jose, CA

Print_ISBN

0-7695-1119-8

Type

conf

DOI

10.1109/ICDM.2001.989522

Filename

989522