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Related Experiment Video

Updated: Nov 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Simple Stopping Criteria for Information Theoretic Feature Selection.

Shujian Yu1, José C Príncipe1

  • 1Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL 32611, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces two novel stopping criteria for information theoretic feature selection, utilizing conditional mutual information (CMI) to optimize feature subsets and improve generalization error. These criteria enhance greedy search strategies by simplifying CMI computation.

Keywords:
conditional mutual informationfeature selectionmultivariate matrix-based Rényi’s α-entropy functionalstopping criterion

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Area of Science:

  • Machine Learning
  • Information Theory
  • Data Science

Background:

  • Feature selection is crucial for minimizing generalization error by identifying optimal feature subsets.
  • Information theory-based methods maximize mutual information between features and class labels but face optimization challenges.
  • Determining optimal subset size and effective stopping criteria for greedy feature selection remains an open problem.

Purpose of the Study:

  • To propose two novel stopping criteria for information theoretic feature selection methods.
  • To address the challenge of automatic optimal subset size determination in greedy feature selection.
  • To enhance the practical implementation of information theoretic feature selection.

Main Methods:

  • Developed two stopping criteria based on monitoring conditional mutual information (CMI) among variable groups.
  • Employed a recently developed multivariate matrix-based Rényi's α-entropy functional for direct data sample estimation.
  • Demonstrated that CMI can be computed without decomposition or approximation, facilitating easy integration.

Main Results:

  • The proposed stopping criteria effectively monitor CMI among groups of variables.
  • The multivariate matrix-based Rényi's α-entropy functional allows for direct and accurate CMI estimation from data.
  • The method simplifies CMI computation, making the criteria easy to implement and integrate into existing algorithms.

Conclusions:

  • The suggested stopping criteria offer a practical solution to optimize feature selection in information theoretic approaches.
  • These criteria can be seamlessly integrated into existing greedy search-based feature selection methods.
  • The findings contribute to more efficient and effective feature selection, improving model generalization.