Prototype induction and attribute selection via evolutionary algorithms

Abstract

This paper addresses the issue of reducing the storage requirements on instance-based learning algorithms. Algorithms proposed by other researches use heuristics to prune instances of the training set or modify the instances themselves to achieve a reduced set of instances. This paper presents an alternative way. The presented approach proposes to induce a reduced set of prototypes (partially-defined instances) with evolutionary algorithms. Experiments were performed with GALE, a fine-grained parallel evolutionary algorithm, and other well-known reduction techniques on several data sets. Results suggest that GALE is competitive and robust for inducing sets of partially-defined instances. Moreover, it achieves better reduction rates in storage requirements without losses in generalization accuracy. Simultaneously, if the partially-defined instances induced by GALE are post-processed, results can also be used for attribute selection.