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Proceedings Paper

Evidence combination techniques for robust classification of short-duration oceanic signals
Author(s): Joydeep Ghosh; Steven D. Beck; Chen-Chau Chu
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Paper Abstract

The identification and classification of underwater acoustic signals is an extremely difficult problem because of low SNRs and a high degree of variability in the signals emanated from the same type of sound source. Since different classification techniques have different inductive biases, a single method cannot give the best results for all signal types. Rather, more accurate and robust classification can be obtained by combining the outputs (evidences) of multiple classifiers based on neural network and/or statistical pattern recognition techniques. In this paper, four approaches to evidence combination are presented and compared using realistic oceanic data. The first method uses an entropy-based weighting of individual classifier outputs. The second is based on combination of confidence factors in a manner similar to that used in MYCIN. The other two methods are majority voting and averaging, with little extra computational overhead. All these techniques give better results than those obtained by the best individual classifier, and also provide a basis for detecting outliers and 'false alarms'.

Paper Details

Date Published: 20 August 1992
PDF: 11 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139951
Show Author Affiliations
Joydeep Ghosh, Univ. of Texas/Austin (United States)
Steven D. Beck, Tracor, Inc. (United States)
Chen-Chau Chu, Schlumberger Lab. for Computer Science (United States)


Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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