DocumentCode :
2223318
Title :
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
Link To Document :
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