Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets

MFS, a combining algorithm designed to improve the accuracy of the nearest neighbor (NN) classi er, is presented and it is shown that MFS is able to reduce both bias and variance components of error.

Abstract

Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging or Error Correcting Output Coding, that significantly improve classifiers like decision trees, rule learners, or neural networks. Unfortunately, many combining methods do not improve the nearest neighbor classifier. In this paper, we present MFS, a combining algorithm designed to improve the accuracy of the nearest neighbor (NN) classifier. MFS combines multiple NN classifiers each using only a random subset of features. The experimental results are encouraging: On 25 datasets from the UCI Repository, MFS significantly improved upon the NN, k nearest neighbor (kNN), and NN classifiers with forward and backward selection of features. MFS was also robust to corruption by irrelevant features compared to the kNN classifier. Finally, we show that MFS is able to reduce both bias and variance components of error. 1 INTRODUCTION The nearest neighbor (NN...