Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Simultaneous design and training of ontogenic neural network classifiers
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Self organizing neural networks for financial diagnosis
Decision Support Systems
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Expert Systems with Applications: An International Journal
Soft computing system for bank performance prediction
Applied Soft Computing
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
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
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
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Developing a business failure prediction model via RST, GRA and CBR
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 hybrid approach of DEA, rough set and support vector machines for business failure prediction
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
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
Applying case based reasoning for prioritizing areas of business management
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
With the rapid development of business computing for Chinese listed companies, it is focused on to use case-based reasoning (CBR) in business failure prediction (BFP). Ranking-order case-based reasoning (RCBR) uses ranking-order information among cases to calculate similarity in the framework of k-nearest neighbor. RCBR is sensitive to the choice of features, meaning that optimal features can help it produce better performance. In this research, we attempt to use wrapper approach to find the optimal feature subset for RCBR in BFP. Forward feature selection method and RCBR are combined to construct a new method, namely forward RCBR (FRCBR). The combination is implemented by combining forward feature selection with RCBR as a wrapper module. Hold out method is used to assessing the performance of the classifier. Empirical data were collected from Chinese listed companies in the Shenzhen Stock Exchange and Shanghai Stock Exchange. We employed the standalone RCBR, the classical CBR with Euclidean metric as its heart, the inductive CBR, the two statistical methods of logistic regression and multivariate discriminate analysis (MDA), and support vector machines to make comparisons. For comparative methods, stepwise MDA was employed to select optimal feature subset. Empirical results indicated that FRCBR can produce dominating performance in short-term BFP of Chinese listed companies.