Displaying similar documents to “Effect of prior probabilities on the classificatory performance of parametric and mathematical programming approaches to the two-group discriminant problem.”

A model for credit scoring: an application of discriminant analysis.

Manuel Artís, Montserrat Guillén, José M.ª Martínez (1994)



The application of statistical techniques in decision making, and more specifically for classification requirements, has proved to be adequate in the context of financial problems. In this study, we present the methodology used and the results obtained in the elaboration of a decision-support system for credit assignment. The problem was to provide an automatic tool for a Spanish financial institution that needed to quantify and analyse credit applications from clients. Firstly, we shall...

Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model

Carmen Gervet, Mohammad Atef (2013)

RAIRO - Operations Research - Recherche Opérationnelle


Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno−economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make...

A classical decision theoretic perspective on worst-case analysis

Moshe Sniedovich (2011)

Applications of Mathematics


We examine worst-case analysis from the standpoint of classical Decision Theory. We elucidate how this analysis is expressed in the framework of Wald's famous Maximin paradigm for decision-making under strict uncertainty. We illustrate the subtlety required in modeling this paradigm by showing that information-gap's robustness model is in fact a Maximin model in disguise.

Learning the naive Bayes classifier with optimization models

Sona Taheri, Musa Mammadov (2013)

International Journal of Applied Mathematics and Computer Science


Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three...