{"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":null,"pages":null},"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\":null,\"pages\":null},\"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}
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.