Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

Abstract

A two-step procedure for nonparametric multiclass classifier design is described. A multiclass recursive partioning algorithm is given which generated a single binary decision tree for classifying all classes. The algorithm minimizes the Bayes risk at each node. A tree termination algorithm is given which optimally terminates binary decision trees. The algorithm yields the unique tree with fewest nodes which minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1984
Accession Number
ADA151361

Entities

People

  • S. Gelfand
  • Sanjoy K. Mitter

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Classification
  • Computer Science
  • Demographic Cohorts
  • Distribution Functions
  • Electrical Engineering
  • Engineering
  • Intervals
  • Machine Learning
  • Military Research
  • Probability
  • Probability Distribution Functions
  • Probability Distributions
  • Terminals
  • Training

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.
  • Statistical inference.