Integrated sensing and processing decision trees.
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.
Priebe
Carey E
CE
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. cep@jhu.edu
Marchette
David J
DJ
Healy
Dennis M
DM
D000465:Algorithms; D001185:Artificial Intelligence; D003661:Decision Support Techniques; D010363:Pattern Recognition, Automated / Q000379:methods*; D018511:Systems Integration