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Integrated Sensing and Processing Decision Trees
June 2004 (vol. 26 no. 6)
pp. 699-708

Abstract—We introduce a methodology for adaptive sequential sensing and processing in a classification setting. Our objective for sensor optimization is the back-end performance metric—in this case, misclassification rate. Our methodology, which we dub Integrated Sensing and Processing Decision Trees (ISPDT), optimizes adaptive sequential sensing for scenarios in which sensor and/or throughput constraints dictate that only a small subset of all measurable attributes can be measured at any one time. Our decision trees optimize misclassification rate by invoking a local dimensionality reduction-based partitioning metric in the early stages, focusing on classification only in the leaves of the tree. We present the ISPDT methodology and illustrative theoretical, simulation, and experimental results.

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Index Terms:
Classification, clustering, adaptive sensing, sequential sensing, local dimensionality reduction.
Citation:
Carey E. Priebe, David J. Marchette, Dennis M. Healy, "Integrated Sensing and Processing Decision Trees," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 699-708, June 2004, doi:10.1109/TPAMI.2004.12
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