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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.
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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