Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels-Reference-Cited by-同舟云学术

Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Gerych Walter¹,Hartvigsen Thomas²,Buquicchio Luke¹,Agu Emmanuel¹,Rundensteiner Elke¹

Affiliation:

1. Worcester Polytechnic Institute, Worcester, MA, USA

2. Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

DARPA WASH Program

U.S. Dept. of Education

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557438

Reference58 articles.

1. A. H. Akbarnejad and M. S. Baghshah. 2019. An Efficient Semi-Supervised Multi-label Classifier Capable of Handling Missing Labels. IEEE TKDE (2019). A. H. Akbarnejad and M. S. Baghshah. 2019. An Efficient Semi-Supervised Multi-label Classifier Capable of Handling Missing Labels. IEEE TKDE (2019).

2. Jessa Bekker and Jesse Davis. 2018. Estimating the class prior in positive and unlabeled data through decision tree induction. In AAAI. Jessa Bekker and Jesse Davis. 2018. Estimating the class prior in positive and unlabeled data through decision tree induction. In AAAI.

3. Jessa Bekker and Jesse Davis . 2020. Learning from positive and unlabeled data: A survey. Machine Learning ( 2020 ). Jessa Bekker and Jesse Davis. 2020. Learning from positive and unlabeled data: A survey. Machine Learning (2020).

4. Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data

5. Matthew R Boutell , Jiebo Luo , Xipeng Shen , and Christopher M Brown . 2004. Learning multi-label scene classification. Pattern Recognition ( 2004 ). Matthew R Boutell, Jiebo Luo, Xipeng Shen, and Christopher M Brown. 2004. Learning multi-label scene classification. Pattern Recognition (2004).