A novel bat algorithm fuzzy classifier approach for classification problems

Shruti Parashar, J. Senthilnath, Xin-She Yang
{"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}
引用次数: 12

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种新的蝙蝠算法模糊分类器分类方法
本文研究了自然启发算法(NIA)与模糊分类器的应用。用于分析的四种算法分别是遗传算法、粒子群算法、人工蜂群算法和蝙蝠算法。在三个标准基准数据集和一个实时多光谱卫星数据集上使用了这些算法。分析了不同模糊nias的计算结果。最后,我们观察到模糊分类器在给定的一组参数下,当应用bat算法时,表现得更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Path management strategy to reduce flooding of grid fisheye state routing protocol in mobile ad hoc network using fuzzy and rough set theory A novel cryptosystem based on cooperating distributed grammar systems Analysis of an M/G/1 retrial queue with Bernoulli vacation, two types of service and starting failure Array P system with t-communicating and permitting mate operation Two-dimensional double jumping finite automata
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1