Attribute Selection with a Multi-objective Genetic Algorithm
作者： Gisele L. Pappa , Alex A. Freitas , Celso A. A. Kaestner
关键词:
摘要: In this paper we address the problem of multi-objective attribute selection in data mining. We propose a genetic algorithm (GA) based on wrapper approach to discover best subset attributes for given classification algorithm, namely C4.5, well-known decision-tree algorithm. The two objectives be minimized are error rate and size tree produced by C4.5. proposed GA is method sense that it discovers set non-dominated solutions (attribute subsets), according concept Pareto dominance.
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