A Novel Recursive Partitioning Criterion

Abstract

A data-driven algorithm for partitioning many-class classification problems is presented. The algorithm generates tree-structured hybrid networks with controller nets at tree branches and local expert nets at the leaves. The controller nets recursively partition the feature space according to a novel misclassification minimization rule designed to create groupings of the classes which simplify the classification task. Each local expert is trained only on a subset of the training data corresponding to one of the partitions. The advantage to this approach is that the classification task that each local expert performs is greatly simplified. This simplification helps to avoid the curse of dimensionality and scaling problems by allowing the local expert nets to focus their search for structure in a small portion of the input space.... Cart, Recursive partitioning, Hybrid networks, Misclassification matrix.

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Document Details

Document Type
Technical Report
Publication Date
Dec 23, 1992
Accession Number
ADA259921

Entities

People

  • Michael P. Perrone

Organizations

  • Brown University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Character Recognition
  • Classification
  • Computing System Architectures
  • Learning
  • Mathematics
  • Military Research
  • Network Architecture
  • Network Topology
  • Neural Networks
  • Optical Character Recognition
  • Probability
  • Security
  • United States
  • United States Government
  • Universities

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.

Technology Areas

  • Space