Neural Networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Computers and Operations Research
Hybrid approaches for classification under information acquisition cost constraint
Decision Support Systems
IEEE Transactions on Neural Networks
The method for solving two types of errors in customer segmentation on unbalanced data
Proceedings of the 10th international conference on Electronic commerce
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Exhaustive and heuristic search approaches for learning a software defect prediction model
Engineering Applications of Artificial Intelligence
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Probabilistic estimation of software size and effort
Expert Systems with Applications: An International Journal
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
Multiple costs based decision making with back-propagation neural networks
Decision Support Systems
Hi-index | 12.05 |
We propose a bisection method for varying classification threshold value for cost sensitive neural network learning. Using simulated data and different misclassification cost asymmetries, we test the proposed threshold varying bisection method and compare it with the traditional fixed-threshold method based neural network and a probabilistic neural network. The results of our experiments illustrate that the proposed threshold varying bisection method performs better than the traditional fixed-threshold method based neural network. However, when compared to probabilistic neural network, the proposed method works well only when the misclassification cost asymmetries are low.