{"title":"多标签分类的广义混合框架","authors":"Charmgil Hong, Iyad Batal, Milos Hauskrecht","doi":"10.1137/1.9781611974010.80","DOIUrl":null,"url":null,"abstract":"<p><p>We develop a novel probabilistic ensemble framework for multi-label classification that is based on the <i>mixtures-of-experts</i> architecture. In this framework, we combine multi-label classification models in the <i>classifier chains family</i> that decompose the class posterior distribution <i>P</i>(<i>Y</i><sub>1</sub>, …, <i>Y<sub>d</sub></i> |<b>X</b>) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.</p>","PeriodicalId":74533,"journal":{"name":"Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657574/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Generalized Mixture Framework for Multi-label Classification.\",\"authors\":\"Charmgil Hong, Iyad Batal, Milos Hauskrecht\",\"doi\":\"10.1137/1.9781611974010.80\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We develop a novel probabilistic ensemble framework for multi-label classification that is based on the <i>mixtures-of-experts</i> architecture. In this framework, we combine multi-label classification models in the <i>classifier chains family</i> that decompose the class posterior distribution <i>P</i>(<i>Y</i><sub>1</sub>, …, <i>Y<sub>d</sub></i> |<b>X</b>) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.</p>\",\"PeriodicalId\":74533,\"journal\":{\"name\":\"Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657574/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1137/1.9781611974010.80\",\"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 ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1137/1.9781611974010.80","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Generalized Mixture Framework for Multi-label Classification.
We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd |X) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.