C4.5: programs for machine learning
C4.5: programs for machine learning
Future Generation Computer Systems
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Ant Colony Optimization
A new version of the ant-miner algorithm discovering unordered rule sets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Extensions to the ant-miner classification rule discovery algorithm
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Editorial survey: swarm intelligence for data mining
Machine Learning
A new classification-rule pruning procedure for an ant colony algorithm
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Classification rule mining with an improved ant colony algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
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The cAnt-Miner algorithm is an Ant Colony Optimization (ACO) based technique for classification rule discovery in problem domains which include continuous attributes. In this paper, we propose several extensions to cAnt-Miner. The main extension is based on the use of multiple pheromone types, one for each class value to be predicted. In the proposed @mcAnt-Miner algorithm, an ant first selects a class value to be the consequent of a rule and the terms in the antecedent are selected based on the pheromone levels of the selected class value; pheromone update occurs on the corresponding pheromone type of the class value. The pre-selection of a class value also allows the use of more precise measures for the heuristic function and the dynamic discretization of continuous attributes, and further allows for the use of a rule quality measure that directly takes into account the confidence of the rule. Experimental results on 20 benchmark datasets show that our proposed extension improves classification accuracy to a statistically significant extent compared to cAnt-Miner, and has classification accuracy similar to the well-known Ripper and PART rule induction algorithms.