基于支持向量机的缺失数据处理方法

Yang Li-hua, Ni Qing-hua
{"title":"基于支持向量机的缺失数据处理方法","authors":"Yang Li-hua, Ni Qing-hua","doi":"10.1109/ICSSEM.2011.6081198","DOIUrl":null,"url":null,"abstract":"This paper systematically analyzes the causes and the mechanism of missing data, and research the processing method of missing data based on the support vector machine. And the results show that the prediction based on support vector machine method is more desirable than neural network, wavelet network model. And this method can promote and apply in the prediction of missing data to a certain extend.","PeriodicalId":406311,"journal":{"name":"2011 International Conference on System science, Engineering design and Manufacturing informatization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Based on support vector machine approach to missing data\",\"authors\":\"Yang Li-hua, Ni Qing-hua\",\"doi\":\"10.1109/ICSSEM.2011.6081198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper systematically analyzes the causes and the mechanism of missing data, and research the processing method of missing data based on the support vector machine. And the results show that the prediction based on support vector machine method is more desirable than neural network, wavelet network model. And this method can promote and apply in the prediction of missing data to a certain extend.\",\"PeriodicalId\":406311,\"journal\":{\"name\":\"2011 International Conference on System science, Engineering design and Manufacturing informatization\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on System science, Engineering design and Manufacturing informatization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSEM.2011.6081198\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on System science, Engineering design and Manufacturing informatization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSEM.2011.6081198","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

本文系统地分析了缺失数据产生的原因和机制,研究了基于支持向量机的缺失数据处理方法。结果表明,基于支持向量机方法的预测效果优于神经网络、小波网络模型。该方法可以在一定程度上促进和应用于缺失数据的预测。
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
Based on support vector machine approach to missing data
This paper systematically analyzes the causes and the mechanism of missing data, and research the processing method of missing data based on the support vector machine. And the results show that the prediction based on support vector machine method is more desirable than neural network, wavelet network model. And this method can promote and apply in the prediction of missing data to a certain extend.
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
EXTRACTOR: An extensible framework for identifying Aspect-Oriented refactoring opportunities Scenario simulation of Sino-Singapore Tianjin Eco-city development based on System Dynamics Face recognition based on classifier combinations Computer aided design and manufacture of high precision cam Design of wireless sensor networks for density of natural gas
×
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