The decision tree approach to classification

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers

Diagnosing multiple faults.

Abstract Diagnostic tasks require determining the differences between a model of an artifact and the artifact itself. The differences between the manifested behavior of the artifact and the predicted behavior of the model guide the search for the differences between the artifact and its model. The diagnostic procedure presented in this paper is model-based, inferring the behavior of the composite device from knowledge of the structure and function of the individual components comprising the device. The system (GDE -General Diagnostic Engine) has been implemented and tested on many examples in the domain of troubleshooting digital circuits. This research makes several novel contributions: First, the system diagnoses failures due to multiple faults. Second, failure candidates are represented and manipulated in terms of minimal sets of violated assumptions, resulting in an efficient diagnostic procedure. Third, the diagnostic procedure is incremental, exploiting the iterative nature of diagnosis. Fourth, a clear separation is drawn between diagnosis and behavior prediction, resulting in a domain (and inference procedure) independent diagnostic procedure. Fifth, GDE combines modelbased prediction with sequential diagnosis to propose measurements to localize the faults. The normally required conditional probabilities are computed from the structure of the device and models of its components. This capability results from a novel way of incorporating probabilities and information theory into the context mechanism provided by AssumptionBased Truth Maintenance

Tree Structured Algorithms for Scheduling Activities and Resources in a Continuum of Time

Computing and Information Science