Does linear combination outperform the k-NN rule?

摘要

Some classifier combination experimental results show that the classification error rate of one linear combination method, namely multi-response linear regression is smaller than that of classical k-NN rule. This paper discusses the reason which results in this phenomenon and proposes a new training data set edit approach to improve the performance of the k-NN rule. Our new approach is tested on two large data sets selected from ELENA database and UCI database, the experimental results show it outperform both classical k-NN and linear regression.