Analysis of new techniques to obtain quality training sets

This paper presents new algorithms to identify and eliminate mislabelled, noisy and atypical training samples for supervised learning and more specifically, for nearest neighbour classification. The main goal of these approaches is to enhance the classification accuracy by improving the quality of the training data. Several experiments with synthetic and real data sets are carried out in order to illustrate the behaviour of the schemes proposed here and compare their performance with that of other traditional techniques. It is also analysed the ability of these new algorithms to reduce the possible overlapping among regions of different classes. (C) 2002 Elsevier Science B.V. All rights reserved.