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Summary A previous work explores a Multi-Objective Subset Selection algorithm, denominated the Pareto Front Elite, to induce classifiers. These classifiers are composed by a set of rules selected following Pareto dominance concepts and forming unordered classifiers. These rules are previously created by an association rule algorithm. The performance of the classifiers induced were compared with other...
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...