Simulated Annealing Based Pattern Classification

Abstract

A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in RN N >/= 2, using simulated annealing. It involves generation and placement of a set of hyperplanes (represented by strings) in the feature space that yields minimum misclassification. Theoretical analysis shows that as the size of the training data set approaches infinity, the boundary provided by the simulated annealing based classifier will approach the Bayes boundary. The effectiveness of the classification methodology, along with the generalization ability of the decision boundary, is demonstrated for both artificial data and real life data sets having non-linear/overlapping class boundaries. Results are compared extensively with those of the Bayes classifier, k-NN rule and multilayer perceptron, and Genetic Algorithms, another popular evolutionary technique. Empirical verification of the theoretical claim is also provided.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1998
Accession Number
ADA358039

Entities

People

  • C. A. Murthy
  • S. K. Pal
  • Saumil Bandyopadhyay

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Annealing
  • Artificial Intelligence
  • Boundaries
  • Calorific Value
  • Classification
  • Computations
  • Data Sets
  • Demographic Cohorts
  • Equations
  • Genetic Algorithms
  • Low Temperature
  • Machine Learning
  • Mathematical Analysis
  • Probability
  • Statistical Mechanics
  • Training

Readers

  • Graph Algorithms and Convex Optimization.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology
  • Space