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Related Experiment Video

Updated: May 29, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Bayesian and decision tree approaches for pattern recognition including feature measurement costs.

G R Dattatreya1, V V Sarma

  • 1School of Automation, Indian Institute of Science, Bangalore, India.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study presents a simplified binary decision tree for minimum cost classification, optimizing it with dynamic programming for speech processing tasks like voiced-unvoiced-silence classification.

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Last Updated: May 29, 2026

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Area of Science:

  • Computer Science
  • Signal Processing
  • Machine Learning

Background:

  • Traditional classifiers often involve complex decision graphs.
  • General cost functions in feature measurement and classification present computational challenges.
  • Intermediate rejection of class labels in decision graphs can increase complexity.

Purpose of the Study:

  • To formulate a minimum cost classifier using decision graphs.
  • To develop a heuristic simplification to a binary decision tree.
  • To optimize the binary decision tree using dynamic programming for speech processing.

Main Methods:

  • Formulation of a minimum cost classifier as a decision graph.
  • Application of a heuristic procedure to convert the decision graph into a binary decision tree.
  • Optimization of the binary decision tree using dynamic programming.

Main Results:

  • A simplified binary decision tree classifier is presented.
  • Dynamic programming effectively optimizes the decision tree structure.
  • The method is successfully applied to voiced-unvoiced-silence classification.

Conclusions:

  • The proposed binary decision tree offers a computationally efficient alternative to complex decision graphs.
  • Dynamic programming provides an effective optimization strategy for minimum cost classification trees.
  • This approach is viable for practical speech processing applications.