A Nearest-Neighbour Method with Self-organizing Incremental Neural Network

We introduce a prototype-based nearest-neighbor method that is based on a self-organizing incremental neural network (SOINN). It automatically learns the number of prototypes necessary to determine the decision boundary, and it is robust to noisy training data. The experiments with artificial datasets and real-world datasets illustrate the efficiency of the proposed method.