{"title":"通过标签约束改进多标签分类性能","authors":"Benhui Chen, Xuefen Hong, Lihua Duan, Jinglu Hu","doi":"10.1109/IJCNN.2013.6706861","DOIUrl":null,"url":null,"abstract":"Multi-label classification is an extension of traditional classification problem in which each instance is associated with a set of labels. For some multi-label classification tasks, labels are usually overlapped and correlated, and some implicit constraint rules are existed among the labels. This paper presents an improved multi-label classification method based on label ranking strategy and label constraints. Firstly, one-against-all decomposition technique is used to divide a multilabel classification task into multiple independent binary classification sub-problems. One binary SVM classifier is trained for each label. Secondly, based on training data, label constraint rules are mined by association rule learning method. Thirdly, a correction model based on label constraints is used to correct the probabilistic outputs of SVM classifiers for label ranking. Experiment results on three well-known multi-label benchmark datasets show that the proposed method outperforms some conventional multi-label classification methods.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"543 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Improving multi-label classification performance by label constraints\",\"authors\":\"Benhui Chen, Xuefen Hong, Lihua Duan, Jinglu Hu\",\"doi\":\"10.1109/IJCNN.2013.6706861\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-label classification is an extension of traditional classification problem in which each instance is associated with a set of labels. For some multi-label classification tasks, labels are usually overlapped and correlated, and some implicit constraint rules are existed among the labels. This paper presents an improved multi-label classification method based on label ranking strategy and label constraints. Firstly, one-against-all decomposition technique is used to divide a multilabel classification task into multiple independent binary classification sub-problems. One binary SVM classifier is trained for each label. Secondly, based on training data, label constraint rules are mined by association rule learning method. Thirdly, a correction model based on label constraints is used to correct the probabilistic outputs of SVM classifiers for label ranking. Experiment results on three well-known multi-label benchmark datasets show that the proposed method outperforms some conventional multi-label classification methods.\",\"PeriodicalId\":376975,\"journal\":{\"name\":\"The 2013 International Joint Conference on Neural Networks (IJCNN)\",\"volume\":\"543 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2013 International Joint Conference on Neural Networks (IJCNN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2013.6706861\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2013 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2013.6706861","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving multi-label classification performance by label constraints
Multi-label classification is an extension of traditional classification problem in which each instance is associated with a set of labels. For some multi-label classification tasks, labels are usually overlapped and correlated, and some implicit constraint rules are existed among the labels. This paper presents an improved multi-label classification method based on label ranking strategy and label constraints. Firstly, one-against-all decomposition technique is used to divide a multilabel classification task into multiple independent binary classification sub-problems. One binary SVM classifier is trained for each label. Secondly, based on training data, label constraint rules are mined by association rule learning method. Thirdly, a correction model based on label constraints is used to correct the probabilistic outputs of SVM classifiers for label ranking. Experiment results on three well-known multi-label benchmark datasets show that the proposed method outperforms some conventional multi-label classification methods.