Predicting bank failures: A neural network approach
Applied Artificial Intelligence
Advances in neural information processing systems 2
Ten lectures on wavelets
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Ratio Selection for Classification Models
Data Mining and Knowledge Discovery
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
On the efficiency of the orthogonal least squares training method for radial basis function networks
IEEE Transactions on Neural Networks
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Selecting radial basis function network centers with recursive orthogonal least squares training
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Use of a quasi-Newton method in a feedforward neural network construction algorithm
IEEE Transactions on Neural Networks
Software development cost estimation using wavelet neural networks
Journal of Systems and Software
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
Fuzzy Support Vector Machine for bankruptcy prediction
Applied Soft Computing
Expert Systems with Applications: An International Journal
International Journal of Information Systems and Social Change
Topological pattern discovery and feature extraction for fraudulent financial reporting
Expert Systems with Applications: An International Journal
Hi-index | 0.02 |
This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997--2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.