Evidence combination techniques for robust classification of short-duration oceanic signals

Joydeep Ghosh; Steven D. Beck; Chen-Chau Chu

doi:10.1117/12.139951

20 August 1992 Evidence combination techniques for robust classification of short-duration oceanic signals

Joydeep Ghosh, Steven D. Beck, Chen-Chau Chu

Proceedings Volume 1706, Adaptive and Learning Systems; (1992) https://doi.org/10.1117/12.139951
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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'.

Citation Download Citation

Joydeep Ghosh, Steven D. Beck, and Chen-Chau Chu "Evidence combination techniques for robust classification of short-duration oceanic signals", Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); https://doi.org/10.1117/12.139951

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