stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on scenario generation model about future environment developments. In the present paper, the scenario model is developed for the case when enough data paths can be generated, but due to solvability of stochastic program the scenario tree has to be constructed. The propos...

The paper describes a system that can automatically generate tree adjoining grammars from abstract specifications. Our system is based on the use of tree descriptions to specify a grammar by separately defining pieces of tree structure that encode independent syntactic principles. Various individual specifications are then combined to form the elementary trees of the grammar. The system enables...

I the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree s...

decision trees as one of the data mining techniques, is used in credit scoring of bank customers. the main problem is the construction of decision trees in that they can classify customers optimally. this paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. genetic algorithm can help in credit sco...

To develop practical and efficient scenario tree reduction methods, we introduce a new methodology which allows for changing node values, and an easy-tocalculate distance function to measure the difference between two scenario trees. Based on minimizing the new distance, we first construct a primitive scenario tree reduction model which also minimizes the Wasserstein distance between the reduce...