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A novel logistic regression model combining semi-supervised learning and active learning for disease classification.

Hua Chai1, Yong Liang2, Sai Wang1

  • 1Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, 999078, China.

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This study introduces a novel logistic regression model that combines active learning and semi-supervised learning. This approach effectively utilizes unlabeled biological data to improve disease classification accuracy with minimal cost.

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • Supervised learning models require extensive labeled data, which is often scarce in biological datasets.
  • Manual labeling of biological samples is costly and time-consuming, necessitating efficient data utilization strategies.
  • Existing methods like active learning and semi-supervised learning have limitations, including model bias and susceptibility to noisy data.

Purpose of the Study:

  • To develop a novel logistic regression model that synergistically integrates active learning and semi-supervised learning.
  • To enhance disease classification accuracy by effectively leveraging unlabeled biological data.
  • To minimize the cost associated with utilizing unlabeled samples in machine learning models.

Main Methods:

  • A novel logistic regression model was developed, combining the strengths of active learning and semi-supervised learning.
  • An update pseudo-labeled samples mechanism was incorporated to mitigate the impact of false pseudo-labels.
  • The model was evaluated on its performance in disease classification and gene selection tasks.

Main Results:

  • The proposed model demonstrated superior performance compared to traditional semi-supervised learning and active learning methods.
  • The integration of active learning and semi-supervised learning proved effective in improving disease classification accuracy.
  • The pseudo-labeling update mechanism successfully reduced the number of false pseudo-labeled samples, enhancing model robustness.

Conclusions:

  • The novel logistic regression model offers a cost-effective solution for improving disease classification using limited labeled biological data.
  • The complementarity of active learning and semi-supervised learning provides a robust framework for handling unlabeled data.
  • This approach shows significant potential for applications in disease classification and gene selection within bioinformatics.