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Multi-label Classification Trending Challenges and Approaches

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Emerging Trends in Expert Applications and Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 841))

Abstract

Multi-label classification (MLC) has caught the attention of researchers in various domains. MLC is a classification which assigns multiple labels to a single instance. MLC aims to train the classifier for modern applications such as sentiment classification, news classification, and text classification. MLC problem can be solved by either converting into a single-label problem or by extending machine learning methods for solving it. In this paper, the challenges faced during training the classifier which includes label space dimensionality, label drifting, and incomplete labeling are considered for review. This paper also shows the newly emerged data analysis methods for multi-label data.

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Author information

Authors and Affiliations

  1. Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Pooja Pant, A. Sai Sabitha, Tanupriya Choudhury & Prince Dhingra

Authors
  1. Pooja Pant
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  2. A. Sai Sabitha
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  3. Tanupriya Choudhury
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  4. Prince Dhingra
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Corresponding author

Correspondence to Tanupriya Choudhury .

Editor information

Editors and Affiliations

  1. Jaipur Engineering College and Research Centre, Jaipur, Rajasthan, India

    Vijay Singh Rathore

  2. Intelligent Systems Lab, University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  3. Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  4. Sabar Institute of Technology for Girls, Ahmedabad, Gujarat, India

    Amit Joshi

  5. Department of Computer Science and Engineering, Jaipur Engineering College and Research Centre, Jaipur, Rajasthan, India

    Shikha Maheshwari

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Pant, P., Sai Sabitha, A., Choudhury, T., Dhingra, P. (2019). Multi-label Classification Trending Challenges and Approaches. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_51

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