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