In this paper we address the problem of multi-objective attribute selection in data mining. We propose a multi-objective genetic algorithm (GA) based on the wrapper approach to discover the best subset of attributes for a given classification algorithm, namely C4.5, a well-known decision-tree algorithm. The two objectives to be minimized are the error rate and the size of the tree produced by C4.5. The proposed GA is a multi-objective method in the sense that it discovers a set of non-dominated solutions (attribute subsets), according to the concept of Pareto dominance.
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