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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Minimising added classification error using Walsh coefficients.

Terry Windeatt1, Cemre Zor

  • 1Centre for Vision Speech and Signal Processing, Faculty of Electronics and Physical Sciences, University of Surrey, Guildford, Surrey, UK. t.windeatt@surrey.ac.uk

IEEE Transactions on Neural Networks
|August 5, 2011
PubMed
Summary
This summary is machine-generated.

Learning an incompletely specified Boolean function using classifier ensembles reveals a connection between classification error and Walsh coefficients. Optimal training epochs minimize ensemble test error and maximize mean second-order coefficients.

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Area of Science:

  • Machine Learning
  • Supervised Learning
  • Boolean Functions

Background:

  • Classifier ensembles can be viewed as learning incompletely specified Boolean functions.
  • Walsh coefficients offer insights into Boolean function properties.

Purpose of the Study:

  • To establish a relationship between classification error and Walsh coefficients in supervised learning.
  • To investigate the behavior of second-order Walsh coefficients during ensemble training.

Main Methods:

  • Formulating two-class supervised learning as learning an incompletely specified Boolean function.
  • Utilizing an extended Tumer-Ghosh model to analyze Walsh coefficients.
  • Employing multilayer perceptron base classifiers with varied hidden nodes and epochs.

Main Results:

  • Demonstrated that Walsh coefficients can be estimated without knowledge of unspecified patterns.
  • Established a direct relationship between added classification error and second-order Walsh coefficients.
  • Observed that mean second-order coefficients peak concurrently with minimum ensemble test error.

Conclusions:

  • The study provides a novel perspective on classifier ensembles using Boolean function theory.
  • Second-order Walsh coefficients serve as a valuable indicator of ensemble performance and training progress.