C4.5: programs for machine learning
C4.5: programs for machine learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Nature Inspired Methods in the Radial Basis Function Network Learning Process
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Review: A review of ant algorithms
Expert Systems with Applications: An International Journal
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
Ant colony optimization for nonlinear AVO inversion of network traffic allocation optimization
Expert Systems with Applications: An International Journal
Extensions to the ant-miner classification rule discovery algorithm
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Improving hierarchical document cluster labels through candidate term selection
Intelligent Decision Technologies
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 |
The Ant-Miner algorithm, first proposed by Parpinelli and colleagues, applies an ant colony optimization heuristic to the classification task of data mining to discover an ordered list of classification rules. In this paper we present a new version of the Ant-Miner algorithm, which we call Unordered Rule Set Ant-Miner, that produces an unordered set of classification rules. The proposed version was evaluated against the original Ant-Miner algorithm in six public-domain datasets and was found to produce comparable results in terms of predictive accuracy. However, the proposed version has the advantage of discovering more modular rules, i.e., rules that can be interpreted independently from other rules - unlike the rules in an ordered list, where the interpretation of a rule requires knowledge of the previous rules in the list. Hence, the proposed version facilitates the interpretation of discovered knowledge, an important point in data mining.