基于属性分类和聚类的机器学习分类器群优化技术

T. Vadivu, B. Sumathi
{"title":"基于属性分类和聚类的机器学习分类器群优化技术","authors":"T. Vadivu, B. Sumathi","doi":"10.1109/ICCES45898.2019.9002279","DOIUrl":null,"url":null,"abstract":"Software defined Networking addresses the growth of traffic with static architectures. SDN is a standard which separates the network structures. Quality of Service is used with network traffic to transfer high bandwidth and multimedia information. Fractional Order Darwinian optimization (FODPSO) is used with Particle Swarm Optimization algorithm to enhance the detection accuracy. In this research paper, comparison of different Classification algorithms are used to achieve better performance.","PeriodicalId":348347,"journal":{"name":"2019 International Conference on Communication and Electronics Systems (ICCES)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Swarm Optimization techniques using Attribute assortment and Clustering for Machine Learning Classifiers\",\"authors\":\"T. Vadivu, B. Sumathi\",\"doi\":\"10.1109/ICCES45898.2019.9002279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software defined Networking addresses the growth of traffic with static architectures. SDN is a standard which separates the network structures. Quality of Service is used with network traffic to transfer high bandwidth and multimedia information. Fractional Order Darwinian optimization (FODPSO) is used with Particle Swarm Optimization algorithm to enhance the detection accuracy. In this research paper, comparison of different Classification algorithms are used to achieve better performance.\",\"PeriodicalId\":348347,\"journal\":{\"name\":\"2019 International Conference on Communication and Electronics Systems (ICCES)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Communication and Electronics Systems (ICCES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCES45898.2019.9002279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Communication and Electronics Systems (ICCES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCES45898.2019.9002279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

软件定义的网络通过静态架构解决流量的增长问题。SDN是一种分离网络结构的标准。服务质量与网络流量一起用于传输高带宽和多媒体信息。将分数阶达尔文优化算法(FODPSO)与粒子群优化算法相结合,提高了检测精度。本文对不同的分类算法进行了比较,以获得更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Swarm Optimization techniques using Attribute assortment and Clustering for Machine Learning Classifiers
Software defined Networking addresses the growth of traffic with static architectures. SDN is a standard which separates the network structures. Quality of Service is used with network traffic to transfer high bandwidth and multimedia information. Fractional Order Darwinian optimization (FODPSO) is used with Particle Swarm Optimization algorithm to enhance the detection accuracy. In this research paper, comparison of different Classification algorithms are used to achieve better performance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Automated Library System Using Robotic Arm Road Crack Detection and Segmentation for Autonomous Driving Design and Simulation of Two Stage Sample and Hold Circuit with Low Power using Current Controlled Conveyor The PI Controllers and its optimal tuning for Load Frequency Control (LFC) of Hybrid Hydro-thermal Power Systems Low Power Hardware Based Real Time Music System and Digital Data Transmission Using FPGA
×
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