A kNN classifier optimized by P systems

Abstract

We propose a k-nearest neighbors (kNN) classification algorithm optimized by P systems in this article, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of several cells is considered as its computational framework. Under the control of both evolution rules and communication rules, each cell determines the optimal set of k-nearest neighbors for a sample. The proposed kNN-P is evaluated on 18 benchmark datasets and compared with classical kNN algorithm and 8 recently developed improved algorithms. Comparison results demonstrate the availability and effectiveness of the proposed algorithm.