Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems

Author

Casillas, Jorge ; Orriols-Puig, Albert ; Bernadò-Mansilla, Ester

Author_Institution

Dept. of Comput. Sci. & Artificial Intell., Univ. of Granada, Granada

fYear

2008

fDate

4-7 March 2008

Firstpage

89

Lastpage

94

Abstract

This paper proposes Pitts-DNF-C, a multi- objective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for classification tasks. The system is explicitly designed to only explore solutions that lead to consistent, complete, and compact rule sets without redundancies and inconsistencies. The behavior of the system is analyzed on a collection of real-world data sets, showing its competitiveness in terms of performance and interpretability with respect to three other fuzzy learners.

Keywords

fuzzy reasoning; learning (artificial intelligence); learning systems; pattern classification; DNF-type fuzzy rule-based classification system; Pitts-DNF-C system; multiobjective Pittsburgh-style learning classifier system; Artificial intelligence; Computer science; Fires; Fuzzy sets; Fuzzy systems; Genetic algorithms; Input variables; Knowledge based systems; Performance analysis; Proposals;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolving Systems, 2008. GEFS 2008. 3rd International Workshop on

Conference_Location

Witten-Bommerholz

Print_ISBN

978-1-4244-1612-7

Electronic_ISBN

978-1-4244-1613-4

Type

conf

DOI

10.1109/GEFS.2008.4484573

Filename

4484573