Simulated annealing based pattern classification

Bandyopadhyay, Sanghamitra ; Pal, Sankar K. ; Murthy, C. A. (1998) Simulated annealing based pattern classification Information Sciences, 109 (1-4). pp. 165-184. ISSN 0020-0255

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A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in RN, N ≥2, using Simulated Annealing (SA). 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 SA 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.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Pattern Recognition; Evolutionary Computation; Genetic Algorithm; Bayes Error Probability
ID Code:26119
Deposited On:06 Dec 2010 13:04
Last Modified:17 May 2016 09:27

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