Designing neural networks using genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Neurocomputing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Introduction to artificial neural systems
Introduction to artificial neural systems
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
An empirical validation of a neural network model for software effort estimation
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
Behavioral assessment of recoverable credit of retailer's customers
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Marketing Optimization in Retail Banking
Interfaces
An integrated data mining model for customer credit evaluation
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
A dual hybrid forecasting model for support of decision making in healthcare management
Advances in Engineering Software
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Recently, promising results with neural networks have been reported for two-group classification problems such as bankruptcy prediction and thrift failures. Such applications are usually characterized by unequal frequencies of the two states of interest. This creates a major obstacle to effective performance evaluation of various decision models. Critical issues affecting the comparison include training sample design and the use of an appropriate performance metric. This paper addresses these two issues by comparing the performance of neural networks with that of statistical models for the decision problem of identifying successful new ventures.