Forecasting with neural networks
Information and Management
Self organizing neural networks for financial diagnosis
Decision Support Systems
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Computers and Operations Research
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Expert Systems with Applications: An International Journal
An integrative model with subject weight based on neural network learning for bankruptcy prediction
Expert Systems with Applications: An International Journal
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
A selective ensemble based on expected probabilities for bankruptcy 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
Ensemble with neural networks for bankruptcy prediction
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
An application of support vector machine to companies' financial distress prediction
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios
Knowledge-Based Systems
Ensemble methods for advanced skier days prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Due to the important role of financial distress prediction (FDP) for enterprises, it is crucial to improve the accuracy of FDP model. In recent years, classifier ensemble has shown promising advantage over single classifier, but the study on classifier ensemble methods for FDP is still not comprehensive enough and leaves to be further explored. This paper constructs AdaBoost ensemble respectively with single attribute test (SAT) and decision tree (DT) for FDP, and empirically compares them with single DT and support vector machine (SVM). After designing the framework of AdaBoost ensemble method for FDP, the article describes AdaBoost algorithm as well as SAT and DT algorithm in detail, which is followed by the combination mechanism of multiple classifiers. On the initial sample of 692 Chinese listed companies and 41 financial ratios, 30 times of holdout experiments are carried out for FDP respectively one year, two years, and three years in advance. In terms of experimental results, AdaBoost ensemble with SAT outperforms AdaBoost ensemble with DT, single DT classifier and single SVM classifier. As a conclusion, the choice of weak learner is crucial to the performance of AdaBoost ensemble, and AdaBoost ensemble with SAT is more suitable for FDP of Chinese listed companies.