Title
A correlation-based ant miner for classification rule discovery
Abstract
In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.
Year
DOI
Venue
2012
10.1007/s00521-010-0490-5
Neural Computing and Applications
Keywords
DocType
Volume
original AntMiner algorithm,new ACO-based classification algorithm,accuracy rate,classification rule discovery,correlation-based ant miner,rule set,ant colony optimization meta-heuristic,proposed approach,average term,average number,pruning redundant rule
Journal
21
Issue
ISSN
Citations 
2
1433-3058
7
PageRank 
References 
Authors
0.48
9
2
Name
Order
Citations
PageRank
Abdul Rauf Baig112615.82
Waseem Shahzad2708.91