一个由P系统优化的kNN分类器

Juan Hu, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, Xiaohui Luo
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引用次数: 5

摘要

本文提出了一种由P个系统优化的k近邻(kNN)分类算法,称为kNN-P,它可以提高原有kNN分类器的性能。考虑由多个单元组成的P系统作为其计算框架。在进化规则和通信规则的共同控制下,每个细胞确定一个样本的最优k近邻集。在18个基准数据集上对所提出的kNN- p算法进行了评估,并与经典kNN算法和8种新开发的改进算法进行了比较。对比结果证明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A kNN classifier optimized by P systems
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.
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