The nature of statistical learning theory
The nature of statistical learning theory
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Olfactory Classification via Interpoint Distance Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
Designing decision trees with the use of fuzzy granulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.