Paper Info
Title
Hybrid Ant Colony Optimization and Simulated Annealing for Rule Induction
Abstract
This paper proposes a hybrid of ant colony optimization and simulated annealing for rule induction. The hybrid algorithm is part of the sequential covering algorithm which is the commonly used algorithm to extract classification rules directly from data. The hybrid algorithm will minimize the problem of low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule. Simulated Annealing will be used to produce a rule for each ant. The best rule for a colony will then be chosen and later the best rule among the colonies will be included in the rule set. The ordered rule set is arranged in decreasing order of generation. Thirteen data sets which consist of discrete and continuous data from UCI repository were used to evaluate the performance of the proposed algorithm. Promising results were obtained when compared to the Ant-Miner algorithm in terms of accuracy, number of rules and number of terms in the rules.
Year
DOI
Venue
2011
10.1109/EMS.2011.17
Computer Modeling and Simulation
Keywords
Field
DocType
hybrid ant colony optimization,best quality rule,hybrid algorithm,best rule,ant colony optimization,simulated annealing,rule set,rule induction,ant-miner algorithm,proposed algorithm,classification rule,classification
Simulated annealing,Ant colony optimization algorithms,Data mining,Data set,Hybrid algorithm,Computer science,Algorithm,Adaptive simulated annealing,Rule induction,Metaheuristic
Conference
ISSN
ISBN
Citations 
2473-3539
978-1-4673-0060-5
1
PageRank 
References 
Authors
0.39
8
2
Name
Order
Citations
PageRank
Rizauddin Saian111.40
Ku Ruhana Ku Mahamud2229.33