Optimal decision rules for distributed binary decision tree classifiers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 system, 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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Pages14-22
Number of pages9
Volume4051
StatePublished - 2000
EventSensor Fusion: Architectures, Algorithms, and Applications IV - Orlando, FL, USA
Duration: Apr 25 2000Apr 28 2000

Other

OtherSensor Fusion: Architectures, Algorithms, and Applications IV
CityOrlando, FL, USA
Period4/25/004/28/00

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Zhang, Q., & Varshney, P. K. (2000). Optimal decision rules for distributed binary decision tree classifiers. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 4051, pp. 14-22). SPIE.