Abstract
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 multi-objective 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.
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Ishida, C.Y., de Carvalho, A.B., Pozo, A.T.R., Goldbarg, E.F.G., Goldbarg, M.C. (2008). Exploring Multi-objective PSO and GRASP-PR for Rule Induction. In: van Hemert, J., Cotta, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2008. Lecture Notes in Computer Science, vol 4972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78604-7_7
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DOI: https://doi.org/10.1007/978-3-540-78604-7_7
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