Showing papers on "Decision tree learning published in 1981"

Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

Abstract: The minimum cost classifier when general cost functions are associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimization of the binary tree in this context is carried out using dynamic programming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Multistage classification of multispectral Earth observational data: The design approach

Abstract: An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure.