Constructing optimal classifiers from sub-optimal composite hypothesis tests

Alan Schaum

doi:10.1117/12.2556105

14 May 2020 Constructing optimal classifiers from sub-optimal composite hypothesis tests

Alan Schaum

Proceedings Volume 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI; 113920W (2020) https://doi.org/10.1117/12.2556105
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The general form of admissible solutions to classification problems depends on parameters π_i whose values determine performance. However, translating performance requirements into parameter choices requires a difficult evaluation of interdependent probabilities. In this report we build optimal classifiers by combining composite hypothesis tests. The process relates the parameters π_i to detection thresholds λ _jk , which are more directly predictive of detection and false alarm probabilities. It is found that the constituent composite hypothesis tests cannot be optimal, but instead must be constructed via clairvoyant fusion principles.

Citation Download Citation

Alan Schaum "Constructing optimal classifiers from sub-optimal composite hypothesis tests", Proc. SPIE 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI, 113920W (14 May 2020); https://doi.org/10.1117/12.2556105

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