Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption

Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption

0.00 Avg rating0 Votes
Article ID: iaor20043294
Country: Netherlands
Volume: 36
Issue: 1
Start Page Number: 117
End Page Number: 136
Publication Date: Sep 2003
Journal: Decision Support Systems
Authors: ,
Keywords: personnel & manpower planning, health services
Abstract:

In this paper, we show than when an artificial neural network (ANN) model is used for learning monotonic forecasting functions, it may be useful to screen training data so the screened examples approximately satisfy the monotonicity property. We show how a technical efficiency-based ranking, using the data envelopment analysis (DEA) model, and a predetermined threshold efficiency, might be useful to screen training data so that a subset of examples that approximately satisfy the monotonicity property can be identified. Using a health care forecasting problem, the monotonicity assumption, and a predetermined threshold efficiency level, we use DEA to split training data into two mutually exclusive, “efficient” and “inefficient”, training data subsets. We compare the performance of the ANN by using the “efficient” and “inefficient” training data subsets. Our results indicate that the predictive performance of an ANN that is trained on the “efficient” training data subset is higher than the predictive performance of an ANN that is trained in the “inefficient” training data subset.

Reviews

Required fields are marked *. Your email address will not be published.