Juan Hu, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, Xiaohui Luo
{"title":"一个由P系统优化的kNN分类器","authors":"Juan Hu, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, Xiaohui Luo","doi":"10.1109/FSKD.2017.8393307","DOIUrl":null,"url":null,"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.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"48 9","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A kNN classifier optimized by P systems\",\"authors\":\"Juan Hu, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, Xiaohui Luo\",\"doi\":\"10.1109/FSKD.2017.8393307\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":236093,\"journal\":{\"name\":\"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"volume\":\"48 9\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2017.8393307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2017.8393307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.