Prototype selection for training artificial neural networks based on Fast Condensed Nearest Neighbor rule

Abstract

This paper presents new method for training intelligent networks such as Multi-Layer Perceptron (MLP) and Neuro-Fuzzy Networks (NFN) with prototypes selected via Fast Condensed Nearest Neighbor (FCNN) rule. By applying FCNN, condensed subsets with instances close to the decision boundary are obtained. We call these points High-Priority Prototypes (HPPs) and the network is trained by them. The main objective of this approach is to improve the performance of the classification by boosting the quality of the training-set. The experimental results on several standard classification databases illustrated the power of the proposed method. In comparison to previous approaches which select prototypes randomly, training with HPPs performs better in terms of classification accuracy.