基于有序划分的增强支持向量机文本分类

Yong Shi, Peijia Li, Lingfeng Niu
{"title":"基于有序划分的增强支持向量机文本分类","authors":"Yong Shi, Peijia Li, Lingfeng Niu","doi":"10.1145/3106426.3109428","DOIUrl":null,"url":null,"abstract":"Ordinal regression has received increasing interest in the past years. It aims to classify patterns by an ordinal scale. With the the explosive growth of data, the method of SVM with ordinal partitioning called SVMOP highlights its advantages due to its convenience of dealing with large scale data. However, the method of SVMOP for ordinal regression has not been exploited much. As we know, the costs should be different when dealing with mislabeled samples and how to use them plays a dominant role in model building. However, L2-loss which could enlarge the cost sensitivity has not been applied into SVM ordinal partition yet. In this paper, we propose the method of SVMOP with L2-loss for ordinal regression. Numerical results show that our approach outperforms the method of SVMOP with L1-loss and other ordianl regression models.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Augmented SVM with ordinal partitioning for text classification\",\"authors\":\"Yong Shi, Peijia Li, Lingfeng Niu\",\"doi\":\"10.1145/3106426.3109428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ordinal regression has received increasing interest in the past years. It aims to classify patterns by an ordinal scale. With the the explosive growth of data, the method of SVM with ordinal partitioning called SVMOP highlights its advantages due to its convenience of dealing with large scale data. However, the method of SVMOP for ordinal regression has not been exploited much. As we know, the costs should be different when dealing with mislabeled samples and how to use them plays a dominant role in model building. However, L2-loss which could enlarge the cost sensitivity has not been applied into SVM ordinal partition yet. In this paper, we propose the method of SVMOP with L2-loss for ordinal regression. Numerical results show that our approach outperforms the method of SVMOP with L1-loss and other ordianl regression models.\",\"PeriodicalId\":20685,\"journal\":{\"name\":\"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3106426.3109428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3106426.3109428","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

序数回归在过去几年中受到越来越多的关注。它的目的是按顺序对模式进行分类。随着数据的爆炸式增长,被称为SVMOP的有序划分支持向量机方法因其处理大规模数据的便捷性而凸显出其优势。然而,用于有序回归的SVMOP方法并没有得到太多的应用。正如我们所知,处理错标样本的成本应该是不同的,如何使用它们在模型构建中起着主导作用。而L2-loss会增大代价敏感性,目前还没有应用到支持向量机的有序划分中。本文提出了带L2-loss的SVMOP有序回归方法。数值结果表明,该方法优于具有L1-loss的SVMOP方法和其他正常回归模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Augmented SVM with ordinal partitioning for text classification
Ordinal regression has received increasing interest in the past years. It aims to classify patterns by an ordinal scale. With the the explosive growth of data, the method of SVM with ordinal partitioning called SVMOP highlights its advantages due to its convenience of dealing with large scale data. However, the method of SVMOP for ordinal regression has not been exploited much. As we know, the costs should be different when dealing with mislabeled samples and how to use them plays a dominant role in model building. However, L2-loss which could enlarge the cost sensitivity has not been applied into SVM ordinal partition yet. In this paper, we propose the method of SVMOP with L2-loss for ordinal regression. Numerical results show that our approach outperforms the method of SVMOP with L1-loss and other ordianl regression models.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信