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Reducing variability in the output of pattern classifiers using histogram shaping.

Shalini Gupta1, Chih-Wen Kan, Mia K Markey

  • 1Wireless Terminals Business Unit, Texas Instruments Incorporated, 12500 TI Boulevard, Dallas, Texas 75243, USA. shalini.gupta@ieee.org

Medical Physics
|May 7, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces histogram shaping to reduce variability in pattern classifier outputs and performance metrics like sensitivity and specificity. This technique improves consistency for classifiers with identical ROC curves but different output distributions.

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Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Biomedical Data Analysis

Background:

  • Pattern classifiers with identical Receiver Operating Characteristic (ROC) curves can exhibit variable outputs and performance metrics (sensitivity, specificity).
  • This variability arises from differing output distributions, complicating reliable interpretation and comparison.

Purpose of the Study:

  • To present a novel histogram shaping technique for reducing output variability in pattern classifiers.
  • To decrease variability in sensitivity and specificity pairs at fixed thresholds for classifiers with identical ROC curves but different output distributions.

Main Methods:

  • Identification of variability sources in linear pattern classifier outputs.
  • Theoretical development of a histogram matching technique for classifier outputs.
  • Empirical validation using simulated and real-world mammography data.

Main Results:

  • The proposed classifier output calibration technique significantly reduced variability in sensitivity and specificity pairs across simulated and mammography datasets.
  • Histogram shaping also significantly reduced variability in classifier output values for monotonic or approximately monotonic relationships.

Conclusions:

  • Classifier output calibration via histogram shaping effectively reduces output value and performance metric variability.
  • This method is beneficial for pattern classifiers sharing identical ROC curves but exhibiting differing output distributions.