Reward-punishment editing for mixed data

Abstract

The KNN rule has been widely used in many pattern recognition problems, but it is sensible to noisy data within the training set, therefore, several sample edition methods have been developed in order to solve this problem. A. Franco, D. Maltoni and L. Nanni proposed the Reward-Punishment Editing method in 2004 for editing numerical databases, but it has the problem that the selected prototypes could belong neither to the sample nor to the universe. In this work, we propose a modification based on selecting the prototypes from the training set. To do this selection, we propose the use of the Fuzzy C-means algorithm for mixed data and the KNN rule with similarity functions. Tests with different databases were made and the results were compared against the original Reward-Punishment Editing and the whole set (without any edition). © Springer-Verlag Berlin Heidelberg 2005.