Sample set reduction for nearest neighbor classifiers under different speed requirements

We compare several sample set reduction algorithms for the 1-NN rule with two criteria in mind: classification accuracy and classification speed. The main conclusion is that under aggressive reduction requirements, our scheme with local reduced set selection performs better than conventional algorithms. The results also cast doubt upon the widely used consistency criterion for reduced set generation, especially in noisy domains.