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public:multi-label_classification [2011/02/15 01:21]
htlin
public:multi-label_classification [2021/08/30 07:27] (current)
htlin
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 ====== Introduction ====== ====== Introduction ======
-Multi-label classification is an extension of the traditional ​multi-class classification---the former allows a set of labels to be associated with an instance +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. ​ 
-while the latter allows only one. Applications of multi-label classification naturally ​arise +====== Our Related Works ====== 
-in domains ​such as text mining, vision, or bio-informatics. ​For instance, a document is usually associated ​with more than one category; ​picture often includes many objects; ​gene is usually ​multi-functional.+===== papers ===== 
 + 
 +  * [YY2019] Yao-Yuan YangYi-An Lin, Hong-Min Chu, and Hsuan-Tien Lin. Deep learning with rethinking structure for multi-label classification. In Proceedings of the Asian Conference on Machine Learning (ACML), November 2019. 
 +  * [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. 
 +  * [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. 
 +  * [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. 
 +  * [KH2017] Kuan-Hao Huang and Hsuan-Tien Lin. Cost-sensitive label embedding for multi-label classification. Machine Learning, 106(9--10):​1725--1746,​ October 2017. Also presented in the journal track of ECML '17. 
 +  * [YW2017] Yu-Ping Wu and Hsuan-Tien Lin. Progressive k-labelsets for cost-sensitive multi-label classification. Machine Learning, 106(5):​671--694,​ May 2017. Also presented in the journal track of ACML '16. 
 +  * [CL2014b] Chun-Liang Li and Hsuan-Tien Lin. Condensed filter tree for cost-sensitive multi-label classification. In Proceedings of the International Conference on Machine Learning (ICML), pages 423--431, June 2014. 
 +  * [CF2013] Chun-Sung Ferng and Hsuan-Tien Lin. Multilabel classification using error-correcting codes of hard or soft bits. IEEE Transactions on Neural Networks and Learning Systems, 24(11):​1888--1900,​ November 2013. A shorter version appeared in ACML '11, with code: {{:​public:​ml_ecc.20130515.tgz|}}. 
 +  * [YC2012] Yao-Nan Chen and Hsuan-Tien Lin. Feature-aware label space dimension reduction for multi-label classification. In Advances in Neural Information Processing Systems: Proceedings of the 2012 Conference (NeurIPS), pages 1529--1537, December 2012. 
 +  * [FT2012] Farbound Tai and Hsuan-Tien Lin. Multilabel classification with principal label space transformation. Neural Computation,​ 24(9):​2508--2542,​ September 2012. A preliminary version (under ​mis-spelled title) appeared in the MLD Workshop @ ICML '10, with code: {{public:​llst.20110123.zip|snapshot on 2011/​01/​23}} 
 +  * [CH2011] Chen-Wei Hung and Hsuan-Tien Lin. Multi-label active learning with auxiliary learner. In Proceedings of the Asian Conference on Machine Learning (ACML), volume 20 of JMLR Workshop and Conference Proceedings,​ pages 315--330, 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. 
 + 
 +===== thesis ===== 
 +  * Cheng-Yu Hsieh, A deep model with local surrogate loss for general cost-sensitive ​multi-label learning, Master'​s thesis, 2017. 
 +  * Hsien-Chun Chiu, Multi-label classification with feature-aware cost-sensitive label embedding, Master'​s thesis, 2017. 
 +  * Hong-Min Chu, Dynamic principal projection for cost-sensitive online multi-label classification,​ Master'​s thesis, 2016. 
 +  * Kuo-Hsuan Lo, Cost-sensitive encoding for label space dimension reduction algorithms on multi-label classification,​ Master'​s thesis, 2016. 
 +  * Yi-An Lin, Cyclic classifier chain for cost-sensitive multilabel classification,​ Master'​s thesis, 2015. 
 +  * Kuan-Hao Huang, Cost-sensitive label embedding for multi-label classification,​ Master'​s thesis, 2015. 
 +  * Yu-Ping Wu, Progressive k-labelsets for cost-sensitive multi-label classification,​ Master'​s thesis, 2015. 
 +  * Chun-Liang Li, Condensed filter tree for cost-sensitive multi-label classification,​ Master'​s thesis, 2012. 
 +  * Yao-Nan Chen, Feature-aware label space dimension reduction for multi-label classification,​ Master'​s thesis, 2012. 
 +  * Chun-Sung Ferng, multi-label Classification with hard-/​soft-decoded error-correcting codes, Master'​s thesis, 2012. 
 +  * Chen-Wei Hung, multi-label active learning with auxiliary learner, Master'​s thesis, 2011.
  
-====== Our Related Work ====== 
-===== paper ===== 
-  * Farbound Tai and Hsuan-Tien Lin. Multi-label Classification with Principle Label Space Transformation. National Taiwan University, September 2010; submitted. A preliminary version appeared in MLD Workshop @ ICML '10. [[http://​www.csie.ntu.edu.tw/​~htlin/​paper/​doc/​ml10plst.pdf|link]],​ with code: {{public:​llst.20110123.zip|snapshot on 2011/​01/​23}} 
-  * Farbound Tai and Hsuan-Tien Lin. Multi-label Classification with Principle Label Space Transformation. Second International Workshop on learning from Multi-Label Data @ ICML '10, 2010. [[http://​www.csie.ntu.edu.tw/​~htlin/​paper/​doc/​wsmld10plst.pdf|link]] 
  
public/multi-label_classification.1297732865.txt.gz · Last modified: 2019/06/25 13:26 (external edit)