Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Robust Classification for Imprecise Environments
Machine Learning
Using Rule Sets to Maximize ROC Performance
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Grasp and Path Relinking for 2-Layer Straight Line Crossing Minimization
INFORMS Journal on Computing
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
ROCCER: an algorithm for rule learning based on ROC analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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This paper presents a method of classification rule discovery based on two multiple objective metaheuristics: a Greedy Randomized Adaptive Search Procedure with path-relinking (GRASP-PR), and Multiple Objective Particle Swarm (MOPS). The rules are selected at the creation rule process following Pareto dominance concepts and forming unordered classifiers. We compare our results with other well known rule induction algorithms using the area under the ROC curve. The multiobjective metaheuristic algorithms results are comparable to the best known techniques. We are working on different parallel schemes to handle large databases, these aspects will be subject of future works.