Reducing Data Volume in Instance Based Learning

Abstract

During the training phase of the Nearest-Neighbor (NN) algorithm, considered the most popular Instance-Based Learning (IBL) algorithm, all training instances are stored as the description of the learned concept. IBL algorithms postpone the generalization process, which usually happens during the training phase, until the classification phase starts i.e., when an unclassified data instance needs to be classified. When training sets have a high volume of instances, to store them all becomes unfeasible mainly due to storage requirements. Several IBL algorithms overcome storage related problems by implementing data volume reduction i.e., by storing only a representative subset of the training set. The investigation described in this paper focuses on four IBL algorithms that implement data reduction, which have been empirically evaluated in data sets from the UCI Repository. Their performance, considering storage reduction and classification accuracy, are presented and discussed.