Adaptive sequential Bayesian classification using Page's test

Robert S. Lynch Jr.; Peter K. Willett

doi:10.1117/12.458379

6 March 2002 Adaptive sequential Bayesian classification using Page's test

Proceedings Volume 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI; (2002) https://doi.org/10.1117/12.458379
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In this paper, the previously introduced Mean-Field Bayesian Data Reduction Algorithm is extended for adaptive sequential hypothesis testing utilizing Page's test. In general, Page's test is well understood as a method of detecting a permanent change in distribution associated with a sequence of observations. However, the relationship between detecting a change in distribution utilizing Page's test with that of classification and feature fusion is not well understood. Thus, the contribution of this work is based on developing a method of classifying an unlabeled vector of fused features (i.e., detect a change to an active statistical state) as quickly as possible given an acceptable mean time between false alerts. In this case, the developed classification test can be thought of as equivalent to performing a sequential probability ratio test repeatedly until a class is decided, with the lower log-threshold of each test being set to zero and the upper log-threshold being determined by the expected distance between false alerts. It is of interest to estimate the delay (or, related stopping time) to a classification decision (the number of time samples it takes to classify the target), and the mean time between false alerts, as a function of feature selection and fusion by the Mean-Field Bayesian Data Reduction Algorithm. Results are demonstrated by plotting the delay to declaring the target class versus the mean time between false alerts, and are shown using both different numbers of simulated training data and different numbers of relevant features for each class.

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Robert S. Lynch Jr. and Peter K. Willett "Adaptive sequential Bayesian classification using Page's test", Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); https://doi.org/10.1117/12.458379

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KEYWORDS

Binary data

Feature selection

Quantization

Algorithm development

Data modeling

Detection and tracking algorithms

Computer simulations

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