Distributed Hypothesis Testing Using Local Learning Based Classifiers

In this paper we propose a novel approach to distributed detection using learning-based local classifiers and likelihood ratio test (LRT) based fusion center. Local detector's soft outputs are not restricted to have any probabilistic meaning, so even pure discriminative training method can be used. We propose to estimate the conditional densities of the soft output of the local classifiers to formulate the LRT in the fusion center. Also, we suggest simple censoring schemes that take into account the learning-based approaches problem of the slow convergence of tails of learned distributions. The Neyman Pearson (NP) and the sequential probability radio tests are developed for this approach and NP performance is analyzed. The generality of the proposed procedure is illustrated in an example outside the typical field of sensor networks: the automated infectious tuberculosis (TB) diagnosis using local detections of TB bacilli in microscopic images