Optimal decision rules for distributed binary decision tree classifiers

Qian Zhang; Pramod K. Varshney

doi:10.1117/12.381630

3 April 2000 Optimal decision rules for distributed binary decision tree classifiers

Qian Zhang, Pramod K. Varshney

Proceedings Volume 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV; (2000) https://doi.org/10.1117/12.381630
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

We consider the problem of recognizing M objects using a fusion center with N parallel sensors. Unlike conventional M-ary decision fusion systems, our fusion system breaks a complex M-ary decision fusion problem into a sequence of simpler binary decision fusion problems. In our systems, a binary decision tree (BDT) is employed to hierarchically partition the object space at all system elements. The traversal of the BDT is synchronized by the fusion center. The sensor observations are assumed conditionally independent given the unknown object type. We use a greedy performance criterion in which the probability of error is minimized at individual nodes. Using this performance criterion, we characterize the optimal fusion rules and the optimal sensor rules. We compare our results with some important results on conventional one-stage binary fusion.

Citation Download Citation

Qian Zhang and Pramod K. Varshney "Optimal decision rules for distributed binary decision tree classifiers", Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); https://doi.org/10.1117/12.381630

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