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--- public:multi-label_classification [2019/03/22 19:04] – [paper] htlin
+++ public:multi-label_classification [2024/09/04 04:00] (current) – external edit 127.0.0.1
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 ====== Introduction ======
 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 Work ======
+====== 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.
   * [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.
@@ Line 18: / Line 22: @@
   * [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 =====
-  * Yao-Nan Chen. Feature-aware Label Space Dimension Reduction for Multi-label Classification. Master's Thesis, 2012.
+  * Cheng-Yu Hsieh, A deep model with local surrogate loss for general cost-sensitive multi-label learning, Master's thesis, 2017.
-  * Chun-Sung Ferng. Multi-label Classification with Hard-/soft-decoded Error-correcting Codes. Master's Thesis, 2012.
+  * Hsien-Chun Chiu, Multi-label classification with feature-aware cost-sensitive label embedding, Master's thesis, 2017.
-  * Chen-Wei Hung. Multi-label Active Learning with Auxiliary Learner. Master's Thesis, 2011.
+  * 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.