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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A weighted difference loss approach for enhancing multi-label classification.

Qiong Hu1,2, Masrah Azrifah Azmi Murad3, Azreen Bin Azman3

  • 1Faculty of Computer Science and Information Technology, UPM Lebuh Universiti, 43400, Serdang, Selangor, Malaysia. gs65254@student.upm.edu.my.

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|July 11, 2025
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Summary
This summary is machine-generated.

This study introduces Weighted Difference Loss (WDL) to improve multi-label classification by modeling label relationships. WDL enhances minority class recognition and offers a robust alternative to complex model architectures.

Keywords:
BERTLabel Dependency ModelingLoss Function OptimizationMulti-label Sentiment ClassificationWeighted Difference Loss (WDL)

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Conventional multi-label classification methods often overlook dynamic label relationships and intensity shifts.
  • This limitation hinders performance in tasks like sentiment analysis, where emotions co-occur with nuanced proportions.

Purpose of the Study:

  • To introduce a novel Weighted Difference Loss (WDL) framework to address limitations in multi-label classification.
  • To enhance the recognition of minority classes and improve learning from sparse data.

Main Methods:

  • Transforming labels into a normalized distribution to model relative proportions.
  • Computing learnable, weighted differences to capture inter-label dynamics.
  • Employing label-shuffling augmentation for order-invariant relationship learning.

Main Results:

  • Achieved state-of-the-art performance on four public benchmarks.
  • Substantially improved the recognition of minority classes.
  • Demonstrated effective learning from sparse data by leveraging underlying label structure.

Conclusions:

  • The Weighted Difference Loss (WDL) framework offers a robust, loss-driven alternative to complex architectural modifications.
  • WDL effectively captures inter-label dynamics and improves minority class recognition in multi-label classification.