Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
BP neural network with rough set for short term load forecasting
Expert Systems with Applications: An International Journal
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Financial distress prediction based on serial combination of multiple classifiers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Multiple network fusion using fuzzy logic
IEEE Transactions on Neural Networks
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
On interval type-2 rough fuzzy sets
Knowledge-Based Systems
Knowledge-Based Systems
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Group consensus based on evidential reasoning approach using interval-valued belief structures
Knowledge-Based Systems
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
Structure learning for belief rule base expert system: A comparative study
Knowledge-Based Systems
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
Going-concern prediction using hybrid random forests and rough set approach
Information Sciences: an International Journal
| Hi-index | 0.00 |
It is critical to build an effective prediction model to improve the accuracy of financial distress prediction. Some existing literatures have demonstrated that single classifier has limitations and combination of multiple prediction methods has advantages in financial distress prediction. In this paper, we extend the research of multiple predictions to integrate with rough set and Dempster-Shafer evidence theory. We use rough set to determine the weight of each single prediction method and utilize Dempster-Shafer evidence theory method as the combination method. We discuss the research process for the financial distress prediction based on the proposed method. Finally, we provide an empirical experiment with Chinese listed companies' real data to demonstrate the accuracy of the proposed method. We find that the performance of the proposed method is superior to those of single classifier and other multiple classifiers.