MOPG: a multi-objective evolutionary algorithm for prototype generation

Prototype generation deals with the problem of generating a small set of instances, from a large data set, to be used by KNN for classification. The two key aspects to consider when developing a prototype generation method are: (1) the generalization performance of a KNN classifier when using the prototypes; and (2) the amount of data set reduction, as given by the number of prototypes. Both factors are in conflict because, in general, maximizing data set reduction implies decreasing accuracy and viceversa. Therefore, this problem can be naturally approached with multi-objective optimization techniques. This paper introduces a novel multi-objective evolutionary algorithm for prototype generation where the objectives are precisely the amount of reduction and an estimate of generalization performance achieved by the selected prototypes. Through a comprehensive experimental study we show that the proposed approach outperforms most of the prototype generation methods that have been proposed so far. Specifically, the proposed approach obtains prototypes that offer a better tradeoff between accuracy and reduction than alternative methodologies.