Paper Info
Title
Ant Colony Optimization for Rule Induction with Simulated Annealing for Terms Selection
Abstract
This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set. The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule. The proposed聽聽algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. Seventeen data sets which consist of discrete and continuous data from a UCI repository are used to evaluate the performance of the proposed algorithm. Promising results are obtained when compared to the Ant-Miner algorithm and PART algorithm in terms of average predictive accuracy of the discovered classification rules.
Year
DOI
Venue
2012
10.1109/UKSim.2012.115
UKSim
Keywords
Field
DocType
ant colony optimization,ant colony optimization algorithm,terms selection,proposed algorithm,algorithm minimizes,best quality rule,part algorithm,rule induction,ant-miner algorithm,rule construction,classification rule,simulated annealing algorithm,simulated annealing,classification algorithms,classification,prediction algorithms,data set,data mining,approximation algorithms,accuracy
Simulated annealing,Ant colony optimization algorithms,Data mining,Approximation algorithm,Data set,Computer science,FSA-Red Algorithm,Prediction algorithms,Rule induction,Statistical classification
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
2
Name
Order
Citations
PageRank
Rizauddin Saian111.40
Ku Ruhana Ku Mahamud2229.33