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Optimal threshold estimation for binary classifiers using game theory.

Ignacio Enrique Sanchez1

  • 1Protein Physiology Laboratory, University of Buenos Aires, Buenos Aires, Argentina.

F1000Research
|March 16, 2017
PubMed
Summary

This study proposes a game theory approach to optimize binary classifier thresholds in bioinformatics. Using a minimax strategy, the optimal threshold balances classification costs and positive sample abundance for robust performance.

Keywords:
Binary classifierROC curveaccuracyclass imbalancegame theoryminimax principle.optimal cutoffoptimal threshold

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Game Theory

Background:

  • Bioinformatics algorithms often function as binary classifiers.
  • Performance is typically evaluated using the Area Under the Receiver Operating Characteristic (ROC) Curve.
  • Selecting optimal thresholds is challenging due to variable data distributions and classification costs.

Purpose of the Study:

  • To introduce a novel method for determining optimal operating points for binary classifiers.
  • To leverage game theory principles for robust threshold selection in bioinformatics.
  • To enhance the practical utility of classification algorithms.

Main Methods:

  • Conceptualizing classifiers as players in a zero-sum game.
  • Applying the minimax principle to identify the optimal operating point.
  • Analyzing the intersection of the ROC curve and the descending diagonal in ROC space.

Main Results:

  • The proposed method identifies a threshold yielding minimax accuracy of 1-False Positive Rate (FPR).
  • This threshold corresponds to the intersection point between the ROC curve and the descending diagonal.
  • The method demonstrates robustness against uncertainties in positive sample prevalence and classification costs.

Conclusions:

  • A game theory-based minimax approach provides an optimal and robust method for setting classifier thresholds.
  • The "specificity equals sensitivity" condition maximizes robustness against real-world data variations.
  • This approach offers practical advantages for implementing bioinformatics classification algorithms.