Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Data mining: concepts and techniques
Data mining: concepts and techniques
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Neural Networks
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Computers and Operations Research
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expert Systems with Applications: An International Journal
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Interpretable credit model development via artificial neural networks
International Journal of Intelligent Systems in Accounting and Finance Management
Financial distress early warning based on group decision making
Computers and Operations Research
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Least squares support vector machines ensemble models for credit scoring
Expert Systems with Applications: An International Journal
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Three probabilistic neural network approaches are used for credit screening and bankruptcy prediction: a logistic regression neural network (LRNN), a probabilistic neural network (PNN) and a semi-supervised expectation maximization-based neural network. Using real-world bankruptcy prediction and credit screening datasets, we compare the three probabilistic approaches using various performance criteria of sensitivity, specificity, accuracy, decile lift and area under receiver operating characteristics (ROC) curves. The results of our experiments indicate that the PNN outperforms the other two techniques for decile lift and specificity performance metric. Using the area under ROC curve, we find that for bankruptcy prediction data the PNN outperforms the other two approaches when false positive rates (FPRs) are less than 40 %. LRNN outperforms the other two techniques for FPRs higher than 40 % for bankruptcy data. We observe that the LRNN results are very sensitive to the ratio of examples belonging to two classes in training data and there is a tendency to overfit training data. Copyright © 2012 John Wiley & Sons, Ltd.