{"title":"一种新的蝙蝠算法模糊分类器分类方法","authors":"Shruti Parashar, J. Senthilnath, Xin-She Yang","doi":"10.1504/IJAISC.2017.10005624","DOIUrl":null,"url":null,"abstract":"In this paper, the application of nature-inspired algorithms (NIA) along with fuzzy classifiers is studied. The four algorithms used for the analysis are genetic algorithm, particle swarm optimisation, artificial bee colony and bat algorithm. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results obtained using different fuzzy-NIAs are analysed. Finally, we observe that the fuzzy classifiers under a given set of parameters perform more accurately when applied with the bat algorithm.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A novel bat algorithm fuzzy classifier approach for classification problems\",\"authors\":\"Shruti Parashar, J. Senthilnath, Xin-She Yang\",\"doi\":\"10.1504/IJAISC.2017.10005624\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the application of nature-inspired algorithms (NIA) along with fuzzy classifiers is studied. The four algorithms used for the analysis are genetic algorithm, particle swarm optimisation, artificial bee colony and bat algorithm. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results obtained using different fuzzy-NIAs are analysed. Finally, we observe that the fuzzy classifiers under a given set of parameters perform more accurately when applied with the bat algorithm.\",\"PeriodicalId\":364571,\"journal\":{\"name\":\"Int. J. Artif. Intell. Soft Comput.\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Artif. Intell. Soft Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJAISC.2017.10005624\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Artif. Intell. Soft Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJAISC.2017.10005624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel bat algorithm fuzzy classifier approach for classification problems
In this paper, the application of nature-inspired algorithms (NIA) along with fuzzy classifiers is studied. The four algorithms used for the analysis are genetic algorithm, particle swarm optimisation, artificial bee colony and bat algorithm. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results obtained using different fuzzy-NIAs are analysed. Finally, we observe that the fuzzy classifiers under a given set of parameters perform more accurately when applied with the bat algorithm.