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 Multiclass classification is an important problem in machine learning. It can be used in a variety of applications,​ such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification --- the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the "​politics"​ and "​health"​ class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics. ​ Multiclass classification is an important problem in machine learning. It can be used in a variety of applications,​ such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification --- the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the "​politics"​ and "​health"​ class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics. ​
 ====== Our Related Works ====== ====== Our Related Works ======
-===== paper ===== +===== papers ​===== 
-  * [HC2019] Hong-Min Chu, Kuan-Hao Huang, and Hsuan-Tien Lin. Dynamic principal projection for cost-sensitive online multi-label classification. Machine Learning, 2019. Accepted, to be presented in the journal track of ECML '19. + 
-  * [HC2018] Hsien-Chun Chiu and Hsuan-Tien Lin. Multi-label classification with feature-aware cost-sensitive label embedding. In Proceedings of the Conference on Technologies and Applications for Artificial Intelligence (TAAI), November 2018. +  * [YY2019] Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, and Hsuan-Tien Lin. Deep learning with a rethinking structure for multi-label classification. In Proceedings of the Asian Conference on Machine Learning (ACML), November 2019. 
-  * [YY2018] Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, and Hsuan-Tien Lin. Cost-sensitive reference pair encoding for multi-label learning. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 143--155, June 2018. +  * [HC2019] Hong-Min Chu, Kuan-Hao Huang, and Hsuan-Tien Lin. Dynamic principal projection for cost-sensitive online multi-label classification. Machine Learning, ​108(8--9):​1193--1230,​ September ​2019. Also presented in the journal track of ECML '19
-  * [CH2018] Cheng-Yu Hsieh, Yi-An Lin, and Hsuan-Tien Lin. A deep model with local surrogate loss for general cost-sensitive multi-label learning. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), February 2018.+  * [CH2019] Cheng-Yu Hsieh, Miao Xu, Gang Niu, Hsuan-Tien Lin, and Masashi Sugiyama. A pseudo-label method for coarse-to-fine multi-label learning with limited supervision. In Proceedings of the Workshop on Learning from Limited Labeled Data @ ICLR, May 2019
 +  * [HC2018] Hsien-Chun Chiu and Hsuan-Tien Lin. Multi-label classification with feature-aware cost-sensitive label embedding. In Proceedings of the Conference on Technologies and Applications for Artificial Intelligence (TAAI), pages 40--45, November 2018. 
 +  * [YY2018b] Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, and Hsuan-Tien Lin. Deep learning with a rethinking structure for multi-label classification. In Proceedings of the Workshop on Multi-output Learning @ ACML, November 2018. 
 +  * [YY2018a] Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, and Hsuan-Tien Lin. Cost-sensitive reference pair encoding for multi-label learning. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 143--155, June 2018. 
 +  * [CH2018] Cheng-Yu Hsieh, Yi-An Lin, and Hsuan-Tien Lin. A deep model with local surrogate loss for general cost-sensitive multi-label learning. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 3239--3246, February 2018.
   * [KL2017] Kuo-Hsuan Lo and Hsuan-Tien Lin. Cost-sensitive encoding for label space dimension reduction algorithms on multi-label classification. In Proceedings of the Conference on Technologies and Applications for Artificial Intelligence (TAAI), December 2017.   * [KL2017] Kuo-Hsuan Lo and Hsuan-Tien Lin. Cost-sensitive encoding for label space dimension reduction algorithms on multi-label classification. In Proceedings of the Conference on Technologies and Applications for Artificial Intelligence (TAAI), December 2017.
   * [YL2017] Yi-An Lin and Hsuan-Tien Lin. Cyclic classifier chain for cost-sensitive multilabel classification. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), October 2017.   * [YL2017] Yi-An Lin and Hsuan-Tien Lin. Cyclic classifier chain for cost-sensitive multilabel classification. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), October 2017.
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   * [CF2011] Chun-Sung Ferng and Hsuan-Tien Lin. Multi-label classification with error-correcting codes. In Proceedings of the Asian Conference on Machine Learning (ACML), volume 20 of JMLR Workshop and Conference Proceedings,​ pages 281--295, November 2011.   * [CF2011] Chun-Sung Ferng and Hsuan-Tien Lin. Multi-label classification with error-correcting codes. In Proceedings of the Asian Conference on Machine Learning (ACML), volume 20 of JMLR Workshop and Conference Proceedings,​ pages 281--295, November 2011.
   * [FT2010a] Farbound Tai and Hsuan-Tien Lin. Multi-label classification with principle label space transformation. In Proceedings of the 2nd International Workshop on learning from Multi-Label Data @ ICML '10, June 2010.   * [FT2010a] Farbound Tai and Hsuan-Tien Lin. Multi-label classification with principle label space transformation. In Proceedings of the 2nd International Workshop on learning from Multi-Label Data @ ICML '10, June 2010.
 +
 ===== thesis ===== ===== thesis =====
   * Cheng-Yu Hsieh, A deep model with local surrogate loss for general cost-sensitive multi-label learning, Master'​s thesis, 2017.   * Cheng-Yu Hsieh, A deep model with local surrogate loss for general cost-sensitive multi-label learning, Master'​s thesis, 2017.
public/multi-label_classification.1561469148.txt.gz · Last modified: 2019/06/25 13:25 by 127.0.0.1