Classification rule discovery with ant colony optimization

Author

Liu, Bo ; Abbas, H.A. ; McKay, Bob

Author_Institution

Coll. of Comput. & Inf. Eng., Guangxi Univ., Nanning, China

fYear

2003

fDate

13-16 Oct. 2003

Firstpage

83

Lastpage

88

Abstract

Ant-based algorithms or ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli and colleagues applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant_Miner. In this paper, we present an improvement to Ant_Miner (we call it Ant_Miner3). The proposed version was tested on two standard problems and performed better than the original Ant_Miner algorithm.

Keywords

artificial life; combinatorial mathematics; data mining; multi-agent systems; optimisation; pattern classification; Ant Miner algorithm; ant colony optimization; ant-based algorithm; classification algorithm; classification rule discovery; combinatorial optimization; data mining; knowledge discovery; Ant colony optimization; Artificial intelligence; Computer science; Data mining; Databases; Delta modulation; Educational institutions; Humans; Intelligent agent; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on

Print_ISBN

0-7695-1931-8

Type

conf

DOI

10.1109/IAT.2003.1241052

Filename

1241052