In this paper we propose a k-nearest neighbors (kNN) classifier optimized by P systems, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of multiple 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 test sample. The proposed algorithm is evaluated on eighteen benchmark datasets and compared with classical kNN algorithm and eight recently developed improved algorithms. Experimental results demonstrate the availability and effectiveness of the proposed algorithm. (C) 2020 Elsevier B.V. All rights reserved.
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