Inducing Partially-Defined Instances with 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. Our work presents an alternative way. We propose to induce a reduced set of partially-defined instances with Evolutionary Algorithms. Experiments were performed with GALE, our fine-grained parallel Evolutionary Algorithm, and other well-known reduction techniques on several datasets. Results suggest that Evolutionary Algorithms are competitive and robust for inducing sets of partially-defined instances, achieving better reduction rates in storage requirements without losses in generalization accuracy.