Data reduction for instance-based learning using entropy-based partitioning

Abstract. Instance-based learning methods such as the nearest neigh-bor classifier have proven to perform well in pattern classification in sev-eral fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learn-ing, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy.