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Multi-Label Classification: An Overview

Citations

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Cited by:

  1. Yi-Hui Chen & Eric Jui-Lin Lu & Yu-Ting Lin & Ya-Wen Cheng, 2016. "Document overlapping clustering using formal concept analysis," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(2), pages 28-34.
  2. Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
  3. Radu Cristian Alexandru Iacob & Vlad Cristian Monea & Dan Rădulescu & Andrei-Florin Ceapă & Traian Rebedea & Ștefan Trăușan-Matu, 2020. "AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
  4. Chaker Jebari, 2016. "Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-21, March.
  5. Francisco J. Ribadas-Pena & Shuyuan Cao & Víctor M. Darriba Bilbao, 2022. "Improving Large-Scale k -Nearest Neighbor Text Categorization with Label Autoencoders," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
  6. Josef Schwaiger & Timo Hammerl & Johannsen Florian & Susanne Leist, 2021. "UR: SMART–A tool for analyzing social media content," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1275-1320, December.
  7. Debaere, Steven & Coussement, Kristof & De Ruyck, Tom, 2018. "Multi-label classification of member participation in online innovation communities," European Journal of Operational Research, Elsevier, vol. 270(2), pages 761-774.
  8. Rui Wang & Songhao Wang & Ben Niu, 2025. "Shape prior guided defect pattern classification and segmentation in wafer bin maps," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 319-330, January.
  9. Verwaeren, Jan & Waegeman, Willem & De Baets, Bernard, 2012. "Learning partial ordinal class memberships with kernel-based proportional odds models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 928-942.
  10. Mohanrasu, S.S. & Janani, K. & Rakkiyappan, R., 2024. "A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 3-23.
  11. Azzini, Antonia & Cortesi, Nicola & Marrara, Stefania & Topalović, Amir, 2019. "A Multi-Label Machine Learning Approach to Support Pathologist's Histological Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 197-208, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
  12. Xueying Zhang & Qinbao Song, 2015. "A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-30, April.
  13. Måns Karlsson & Ola Hössjer, 2024. "Classification Under Partial Reject Options," Journal of Classification, Springer;The Classification Society, vol. 41(1), pages 2-37, March.
  14. Junming Yin & Jerry Luo & Susan A. Brown, 2021. "Learning from Crowdsourced Multi-labeling: A Variational Bayesian Approach," Information Systems Research, INFORMS, vol. 32(3), pages 752-773, September.
  15. Tao Shu & Zhiyi Wang & Huading Jia & Wenjin Zhao & Jixian Zhou & Tao Peng, 2022. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
  16. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
  17. Karimi Dehkordi, Mohammadreza & Sattari, Fereshteh & Lefsrud, Lianne, 2025. "Creating an incident investigation framework for a complex socio-technical system: Application of multi-label text classification and Bayesian network structure learning," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  18. Meir, Yuval & Tevet, Ofek & Tzach, Yarden & Hodassman, Shiri & Kanter, Ido, 2024. "Role of delay in brain dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
  19. Bocheng Li & Yunqiu Zhang & Xusheng Wu, 2022. "DLKN-MLC: A Disease Prediction Model via Multi-Label Learning," IJERPH, MDPI, vol. 19(15), pages 1-15, August.
  20. Gross, Ronit & Koresh, Ella & Halevi, Tal & Hodassman, Shiri & Meir, Yuval & Tzach, Yarden & Kanter, Ido, 2025. "Multilabel classification outperforms detection-based technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
  21. Hung-Yi Chen & Po-Chou Shih & Yunsen Wang, 2025. "Exploration of designing an automatic classifier for questions containing code snippets—A case study of Oracle SQL certification exam questions," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-24, January.
  22. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
  23. Huazhen Wang & Xin Liu & Bing Lv & Fan Yang & Yanzhu Hong, 2014. "Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-14, June.
  24. Han Zou & Jing Ge & Ruichao Liu & Lin He, 2023. "Feature Recognition of Regional Architecture Forms Based on Machine Learning: A Case Study of Architecture Heritage in Hubei Province, China," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
  25. Fabian Stöhr, 2024. "Advancing language models through domain knowledge integration: a comprehensive approach to training, evaluation, and optimization of social scientific neural word embeddings," Journal of Computational Social Science, Springer, vol. 7(2), pages 1753-1793, October.
  26. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.
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