Rule acquisition with a genetic algorithm

Author

Cattral, Robert ; Oppacher, Franz ; Deugo, Dwight

Author_Institution

Intelligent Syst. Res. Unit, Carleton Univ., Ottawa, Ont., Canada

Volume

1

fYear

1999

fDate

1999

Abstract

This paper describes the implementation and the functioning of RAGA (rule acquisition with a genetic algorithm), a genetic-algorithm-based data mining system suitable for both supervised and certain types of unsupervised knowledge extraction from large and possibly noisy databases. RAGA differs from a standard genetic algorithm in several crucial respects, including the following: (i) its `chromosomes´ are variable-length symbolic structures, i.e. association rules that may contain n-place predicates (n⩾0), (ii) besides typed crossover and mutation operators, it uses macromutations as generalization and specialization operators to efficiently explore the space of rules, and (iii) it evolves a default hierarchy of rules. Several data mining experiments with the system are described

Keywords

data mining; generalisation (artificial intelligence); genetic algorithms; unsupervised learning; very large databases; association rules; chromosomes; default rule hierarchy; generalization operators; genetic-algorithm-based data mining system; large databases; macromutations; mutation operators; n-place predicates; noisy databases; rule acquisition with genetic algorithm; rule space; specialization operators; supervised knowledge extraction; typed crossover operators; unsupervised knowledge extraction; variable-length symbolic structures; Computer science; Data mining; Deductive databases; Genetic algorithms; Intelligent systems; Machine learning; Machine learning algorithms; Space exploration; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.781916

Filename

781916