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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 Works

paper

  • [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.
  • [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.
  • [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.
  • [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: 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 a mis-spelled title) appeared in the MLD Workshop @ ICML '10, with code: 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.
public/multi-label_classification.txt · Last modified: 2019/06/25 13:25 (external edit)