Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Past, present, and future of decision support technology
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Decision Support Systems
A decision support system for detecting products missing from the shelf based on heuristic rules
Decision Support Systems
Model similarity and robustness in predictions from Bayesian networks
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Model Similarity and Rank-Order Based Classification of Bayesian Networks
Journal of Classification
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Consider a model-based decision support system (DSS) where all the variables involved are binary, each taking on 0 or 1. The system categorizes the probability that a certain variable is equal to 1 conditional on a set of variables in an ascending order of the probability values and predicts for the variable in terms of category levels. Under the condition that all the variables are positively associated with each other, it is shown in this paper that the category levels are robust to the probability values. This robustness is illustrated by a simulated experiment using a variety of model structures where a set of probability values is proposed for a robust classification. A robust classification method is proposed as an alternative when exact or satisfactory probability values are not available.