Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Genetic programming: on the programming of computers by means of natural selection
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Neural networks for pattern recognition
Neural networks for pattern recognition
The potential use of DEA for credit applicant acceptance systems
Computers and Operations Research - Special issue on data envelopment analysis
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
A continuation method for (strongly) monotone variational inequalities
Mathematical Programming: Series A and B
Artificial Neural Networks
Pattern Classification (2nd Edition)
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Bayesian approach to neural-network modeling with input uncertainty
IEEE Transactions on Neural Networks
Bayesian nonlinear model selection and neural networks: a conjugate prior approach
IEEE Transactions on Neural Networks
A Meta heuristic approach for performance assessment of production units
Expert Systems with Applications: An International Journal
An automated procedure for identifying poorly documented object oriented software components
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Efficiency measurement for network systems: IT impact on firm performance
Decision Support Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
DEA based data preprocessing for maximum decisional efficiency linear case valuation models
Expert Systems with Applications: An International Journal
Residual strength prediction of artificially damaged composite laminates based on neural networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we show that 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 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 on the "inefficient" training data subset.