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M-ary Rank Classifier Combination: A Binary Linear Programming Problem.

Vincent Vigneron1, Hichem Maaref1

  • 1Informatique, Bio-informatique et Systèmes Complexes (IBISC) EA 4526, univ Evry, Université Paris-Saclay, 40 rue du Pelvoux, 91020 Evry, France.

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Summary
This summary is machine-generated.

Combining weak rank classifiers improves classification accuracy. This method uses rankings to create a consensus decision, enhancing performance in tasks like cervical cancer detection from cytology images.

Keywords:
HPVaggregationbinary linear programmingcervical cancerclassifier combinationdata fusionindependencemutual informationplurality votingranktotal order

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Area of Science:

  • Machine Learning
  • Computer Vision
  • Medical Diagnostics

Background:

  • Classifier combination aims to improve predictive performance by integrating multiple classifiers.
  • Rank-based outputs offer richer information than single class predictions, especially in multi-class problems.

Purpose of the Study:

  • To propose a novel method for combining weak rank classifiers.
  • To formulate classifier decision combination as a discrete optimization problem.
  • To develop a consensus measure for assessing classifier agreement.

Main Methods:

  • Formulating classifier combination as a discrete optimization problem using candidate class rankings.
  • Developing a consensus function that maps individual rankings to a combined ranking.
  • Proposing an information measure to quantify classifier agreement without requiring training data.

Main Results:

  • The proposed approach transforms the combination problem into a binary linear programming problem.
  • A global optimum solution is achievable for the consensus ranking.
  • Classification rates are significantly improved by combining rank classifiers.

Conclusions:

  • Combining weak rank classifiers offers a robust strategy for enhancing classification performance.
  • The developed method is effective and easily implementable, demonstrated on cervical cancer detection data.
  • The consensus measure provides a valuable tool for evaluating the reliability of the combination rule.