Decision making using multiple models

Decision making using multiple models

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Article ID: iaor20001655
Country: Netherlands
Volume: 114
Issue: 1
Start Page Number: 1
End Page Number: 14
Publication Date: Apr 1999
Journal: European Journal of Operational Research
Authors: , ,
Keywords: neural networks
Abstract:

Many real world business situations require classification decisions that must often be made on the basis of judgment and past performance. In this paper, we propose a decision framework that combines multiple models or techniques in a complementary fashion to provide input to managers who make such decisions on a routine basis. We illustrate the framework by specifically using five different classification techniques – neural networks, discriminant analysis, quadratic discriminant analysis, k-nearest neighbor, and multinominal logistic regression analysis. Application of the decision framework to actual retail department store data shows that it is most useful in those cases where uncertainty is high and a priori classification cannot be made with a high degree of reliability. The proposed framework thus enhances the value of exception reporting, and provides managers additional insights into the phenomenon being studied.

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