Current developments in expert systems
Proceedings of the Second Australian Conference on Applications of expert systems
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Computers and Operations Research
Neural network applications in business: a review and analysis of the literature (1988-95)
Decision Support Systems
An expert decision support system for auditor going concern evaluation
An expert decision support system for auditor going concern evaluation
ICIS '00 Proceedings of the twenty first international conference on Information systems
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
A Query-Driven Approach to the Design and Management of Flexible Database Systems
Journal of Management Information Systems
Human decision-making behavior and modeling effects
Decision Support Systems
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Expert Systems with Applications: An International Journal
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Journal of Management Information Systems
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Analysis of human judgment and decision making provides useful methodologies for examining the human decision process and substantive results. One such methodology is a lens model analysis. We used such a model to study how well a model of expert decisions can capture a valid strategy in the decision process. The study also addresses whether a model of an expert can be more accurate than the expert.The predictive accuracy (predictive validity) of two linear (statistical) models and two nonlinear models of human experts is compared. The results indicate that nonlinear models can capture factors (valid nonlinear strategy) that contribute to the experts' predictive accuracy. However, linear models cannot capture the valid non-linear strategy as well as nonlinear models.One linear model and two nonlinear models performed as well as the overall average of a group of experts. However, all of the models were outperformed by the most accurate expert. By combining validity of decision strategy with characteristics of modeling algorithms, it is possible to explain why certain algorithms perform better than others.