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Ensemble Learning for Underwater Target Classification
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 Title & Authors
Ensemble Learning for Underwater Target Classification
Seok, Jongwon;
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 Abstract
The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.
 Keywords
Active Sonar;Target Classification;Classifier Ensemble;Neural Network;
 Language
Korean
 Cited by
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