The nature of statistical learning theory
The nature of statistical learning theory
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
Majority voting combination of multiple case-based reasoning for financial distress prediction
Expert Systems with Applications: An International Journal
Financial distress prediction based on serial combination of multiple classifiers
Expert Systems with Applications: An International Journal
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
Predicting business failure using forward ranking-order case-based reasoning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Principal component case-based reasoning ensemble for business failure prediction
Information and Management
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
An overview of the use of neural networks for data mining tasks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Financial ratio selection for business crisis prediction
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Orthogonal support vector machine for credit scoring
Engineering Applications of Artificial Intelligence
Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs
International Journal of Advanced Pervasive and Ubiquitous Computing
Novel feature selection methods to financial distress prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.08 |
The support vector machine (SVM) has been applied to the problem of bankruptcy prediction, and proved to be superior to competing methods such as the neural network, the linear multiple discriminant approaches and logistic regression. However, the conventional SVM employs the structural risk minimization principle, thus empirical risk of misclassification may be high, especially when a point to be classified is close to the hyperplane. This paper develops an integrated binary discriminant rule (IBDR) for corporate financial distress prediction. The described approach decreases the empirical risk of SVM outputs by interpreting and modifying the outputs of the SVM classifiers according to the result of logistic regression analysis. That is, depending on the vector's relative distance from the hyperplane, if result of logistic regression supports the output of the SVM classifier with a high probability, then IBDR will accept the output of the SVM classifier; otherwise, IBDR will modify the output of the SVM classifier. Our experimentation results demonstrate that IBDR outperforms the conventional SVM.