C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Ant Colony Optimization
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
On the quest for optimal rule learning heuristics
Machine Learning
Editorial survey: swarm intelligence for data mining
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Improving the interpretability of classification rules discovered by an ant colony algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Ant Colony Optimisation (ACO) has been successfully applied to the classification task of data mining in the form of Ant-Miner. A new extension of Ant-Miner, called cAnt-MinerPB, uses the ACO procedure in a different fashion. The main difference is that the search in cAnt-MinerPB is optimised to find the best list of rules, whereas in Ant-Miner the search is optimised to find the best individual rule at each step of the sequential covering, producing a list of best rules. We aim to improve cAnt-MinerPB in two ways, firstly by dynamically finding the rule quality function which is used while the rules are being pruned, and secondly improving the rule-list quality function which is used to guide the search. We have found that changing the rule quality function has little effect on the overall performance, but that by improving the rule-list quality function we can positively affect the discovered lists of rules.