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Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information.

Atiyeh Mortazavi1, Mohammad Hossein Moattar2

  • 1Department of Computer Engineering, Imam Reza International University, Mashhad, Iran.

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This study introduces a novel multiphase cooperative game theory approach for microarray data classification. The method enhances feature selection stability, improving classification accuracy and addressing data imbalance challenges.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • High-dimensional microarray data presents challenges like low efficiency and overfitting.
  • Effective feature selection is crucial for accurate microarray data classification.

Purpose of the Study:

  • To propose a multiphase cooperative game theoretic feature selection approach for microarray data classification.
  • To enhance the stability and accuracy of feature selection in high-dimensional datasets.

Main Methods:

  • A three-phase approach involving filter-based reduction (mutual information, Fisher ratio), Shapley index with Qualitative Mutual Information (QMI), and a forward selection scheme.
  • Utilizing QMI for feature evaluation to improve stability and handle data imbalance.

Main Results:

  • The proposed method demonstrated improved average classification accuracy across eleven microarray datasets.
  • The approach showed enhanced average stability compared to existing feature selection algorithms.

Conclusions:

  • The multiphase cooperative game theoretic approach with QMI is effective for feature selection in microarray data.
  • This method offers a robust solution for improving classification accuracy and stability, particularly in imbalanced or scarce data scenarios.