{"title":"Generalized k-labelset ensemble for multi-label classification","authors":"Hung-Yi Lo, Shou-de Lin, H. Wang","doi":"10.1109/ICASSP.2012.6288315","DOIUrl":null,"url":null,"abstract":"Label powerset (LP) method is one category of multi-label learning algorithms. It reduces the multi-label classification problem to a multi-class classification problem by treating each distinct combination of labels in the training set as a different class. This paper proposes a basis expansion model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the multi-label ground truth. We derive an analytic solution to learn the coefficients efficiently. We have conducted experiments using several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. The results show that our method has better or competitive performance against other methods.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"43 1","pages":"2061-2064"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2012.6288315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Label powerset (LP) method is one category of multi-label learning algorithms. It reduces the multi-label classification problem to a multi-class classification problem by treating each distinct combination of labels in the training set as a different class. This paper proposes a basis expansion model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the multi-label ground truth. We derive an analytic solution to learn the coefficients efficiently. We have conducted experiments using several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. The results show that our method has better or competitive performance against other methods.