A divide-and-conquer approach to the Pairwise Opposite Class-Nearest Neighbor (POC-NN) algorithm for regression problem

Abstract

This paper presents a method for regression problem based on divide-and-conquer approach to the selection of a set of prototypes from the training set for the nearest neighbor rule. This method aims at detecting and eliminating redundancies in a given data set while preserving the significant data. A reduced prototype set contains Pairwise Opposite Class-Nearest Neighbor (POC-NN) prototypes which are used instead of the whole given data. Before finding POC-NN prototypes, all sampling data have to be separated into two classes by using the criteria through odd and even sampling number of data, then POC-NN prototypes are obtained by iterative separation and analysis of the training data into two regions until each region is correctly grouped and classified. The separability is determined by the POC-NN prototypes essential to define the function approximator for local sampling data locating near these POC-NN prototypes. Experiments and results reported showed the effectiveness of this technique and its performance in both accuracy and prototype rate to those obtained by classical nearest neighbor techniques. © Springer-Verlag Berlin Heidelberg 2006.