A Divide-and-Conquer Approach to the Pairwise Opposite Class-Nearest Neighbor (POC-NN) Algorithm for Regression Problem
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.
KeywordsNear Neighbor Regression Problem Uniform Lattice Selection Prototype Sunspot Data
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