A framework for sensitivity analysis of decision trees
By Bogumil Kaminski, Michal Jakubczyk, Przemysław Szufel. Central European Journal of Operations Research, 2017
437 citations recorded by the source corpus.
In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.
Subfields: Artificial Intelligence, General Decision Sciences, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ff598df2e03c85bf43b1cbfd16352d5202dd367b
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In Artificial Intelligence
In Management Science and Operations Research
Citations in the corpus
- A survey on addressing high-class imbalance in big data (2018, background): “A decision tree is a classifier that is modeled on a tree-like structure of internal nodes, branches, and terminal nodes (class labels) [38].”