M. Rezaei; H. Nezamabadi-pour
Abstract
The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, ...
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The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, using the gravitational search algorithm (GSA), two hybrid schemes have been proposed in which PG and PS problems have been considered together. To evaluate the classification performance of these hybrid models, we have performed a comparative experimental study including a comparison between our proposals and some approaches previously studied in the literature using several benchmark datasets. The experimental results demonstrate that our hybrid approaches outperform most of the competitive methods.
