Issue No. 06 - June (2004 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.12
Carey E. Priebe , IEEE Computer Society
<p><b>Abstract</b>—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 <it>Integrated Sensing and Processing Decision Trees</it> (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.</p>
Classification, clustering, adaptive sensing, sequential sensing, local dimensionality reduction.
C. E. Priebe, D. J. Marchette and D. M. Healy, "Integrated Sensing and Processing Decision Trees," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 699-708, 2004.