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Self-paced ensemble and big data identification: a classification of substantial imbalance computational analysis

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Abstract

This research paper focuses on the challenges associated with learning classifiers from large-scale, highly imbalanced datasets prevalent in many real-world applications. Traditional algorithms learning often need better performance and high computational efficiency when dealing with imbalanced data. Factors such as class imbalance, noise, and class overlap make it demanding to learn effective classifiers. In this study, we propose a novel self-paced ensemble framework for classifying imbalanced data. The framework employs under-sampling to self-harmonize data hardness and build a robust ensemble. Extensive experimental testing demonstrates promising results in handling overlapping classes and skewed distributions while maintaining computational efficiency. The self-paced ensemble method addresses the challenges of high imbalance ratios, class overlap, and noise presence in large-scale imbalanced classification problems. By incorporating the knowledge of these challenges into our learning framework, we establish the concept of classification hardness distribution, and the self-paced ensemble is a revolutionary learning paradigm for massive imbalance categorization, capable of improving the performance of existing learning algorithms on imbalanced data and providing better results for future applications.

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Author information

Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Zhengzhou University, 100, Science Avenue, Zhengzhou, 450001, People’s Republic of China

    Shahzadi Bano, Weimei Zhi & Baozhi Qiu

  2. Xi’an Technological University, Xi’an, People’s Republic of China

    Muhammad Raza

  3. School of Electrical Information Engineering, Zhengzhou University, 100, Science Avenue, Zhengzhou, 450001, People’s Republic of China

    Nabila Sehito

  4. Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210018, People’s Republic of China

    Mian Muhammad Kamal

  5. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Ghadah Aldehim

  6. Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, 11495, Riyadh, Saudi Arabia

    Nuha Alruwais

Authors
  1. Shahzadi Bano
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  2. Weimei Zhi
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  3. Baozhi Qiu
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  4. Muhammad Raza
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  5. Nabila Sehito
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  6. Mian Muhammad Kamal
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  7. Ghadah Aldehim
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  8. Nuha Alruwais
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Contributions

SB Conceptualization, Methodology, Software, Writing - Original Draft, Software. WZ: Conceptualization, Methodology, Software BQ: Conceptualization, Methodology MMK: Software MS: Conceptualization, Methodology GA: Validation, Resources, Writing - Review, Editing NA: Supervision, Project administration MMK,SB,BQ: Validation, Resources, Writing - Review, Editing WZ: Supervision, Project administration, Funding acquisition.

Corresponding author

Correspondence to Weimei Zhi.

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The authors declare no competing interests.

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Bano, S., Zhi, W., Qiu, B. et al. Self-paced ensemble and big data identification: a classification of substantial imbalance computational analysis. J Supercomput 80, 9848–9869 (2024). https://doi.org/10.1007/s11227-023-05828-6

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  • DOI: https://doi.org/10.1007/s11227-023-05828-6

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