Forecasting with neural networks
Information and Management
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Predicting Japanese corporate bankruptcy in terms of financial data using neural network
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Self organizing neural networks for financial diagnosis
Decision Support Systems
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
On the relationship between majority vote accuracy and dependency in multiple classifier systems
Pattern Recognition Letters
Deciding Parameter Values with Case-Based Reasoning
Proceedings of the First United Kingdom Workshop on Progress in Case-Based Reasoning
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Computers and Operations Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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
Mixed feature selection based on granulation and approximation
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
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 similarity weighted voting CBR
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: 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
Expert Systems with Applications: An International Journal
Predicting business failure using forward ranking-order case-based reasoning
Expert Systems with Applications: An International Journal
An application of fuzzy information granulation in the emerging area of online sports
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
Expert Systems with Applications: An International Journal
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Hi-index | 12.06 |
Financial distress prediction of companies is such a hot topic that has called interest of managers, investors, auditors, and employees. Case-based reasoning (CBR) is a methodology for problem solving. It is an imitation of human beings' actions in real life. When employing CBR in financial distress prediction, it can not only provide explanations for its prediction, but also advise how the company can get out of distress based on solutions of similar cases in the past. This research puts forward a multiple case-based reasoning system by majority voting (Multi-CBR-MV) for financial distress prediction. Four independent CBR models, deriving from Euclidean metric, Manhattan metric, grey coefficient metric, and outranking relation metric, are employed to generate the system of Multi-CBR. Pre-classifications of the former four independent CBRs are combined to generate the final prediction by majority voting. We employ two kinds of majority voting, i.e., pure majority voting (PMV) and weighted majority voting (WMV). Correspondingly, there are two deriving Multi-CBR systems, i.e., Multi-CBR-PMV and Multi-CBR-WMV. In the experiment, min-max normalization was used to scale all data into the specific range of [0,1], the technique of grid-search was utilized to get optimal parameters under the assessment of leave-one-out cross-validation (LOO-CV), and 30 hold-out data sets were used to assess predictive performance of models. With data collected from Shanghai and Shenzhen Stock Exchanges, experiment was carried out to compare performance of the two Multi-CBR-MV systems with their composing CBRs and statistical models. Empirical results got satisfying results, which has testified the feasibility and validity of the proposed Multi-CBR-MV for listed companies' financial distress prediction in China.