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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Semi-supervised multi-label classification using an extended graph-based manifold regularization.

Ding Li1, Scott Dick1

  • 1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB Canada T6G 1H9.

Complex & Intelligent Systems
|May 10, 2022
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Summary
This summary is machine-generated.

This study introduces a novel semi-supervised learning algorithm for multi-label classification, extending Manifold Regularization. The new method outperforms existing approaches, even with limited labeled data.

Keywords:
Graph-based learningManifold regularizationMulti-label classificationSemi-supervised learning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Graph-based algorithms are effective for semi-supervised learning.
  • Extending these to multi-label classification remains a challenge.

Purpose of the Study:

  • To develop a simplified extension of Manifold Regularization for multi-label classification.
  • To improve performance using a weighting strategy for ground-truth and induced labels.

Main Methods:

  • Derived a simplified Manifold Regularization extension for multi-label classification.
  • Implemented a weighting strategy to differentiate between instance label types.
  • Conducted experiments on four benchmark multi-label datasets.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to existing semi-supervised multi-label methods.
  • Outperformed state-of-the-art supervised methods even with significant unlabeled data.
  • Achieved better overall performance across various label sparsity levels.

Conclusions:

  • The novel algorithm offers an effective solution for semi-supervised multi-label classification.
  • The weighting strategy enhances model influence from different label types.
  • The approach is competitive with fully supervised methods, even with limited labels.