Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
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
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
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
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Editorial survey: swarm intelligence for data mining
Machine Learning
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Using Ant Programming Guided by Grammar for Building Rule-Based Classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the cAnt-MinerPB classification algorithm
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules) - i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorithm to discover a set of rules (unordered rules). The main motivation for discovering a set of rules is to improve the interpretation of individual rules and evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly-used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms and the cAnt-MinerPB producing ordered rules are also presented.