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Multi-Label Classification: An Overview

Author

Listed:
  • Grigorios Tsoumakas

    (Aristotle University of Thessaloniki, Greece)

  • Ioannis Katakis

    (Aristotle University of Thessaloniki, Greece)

Abstract

Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

Suggested Citation

  • Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
    • Handle: RePEc:igg:jdwm00:v:3:y:2007:i:3:p:1-13
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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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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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