Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Case Representation Issues for Case-Based Reasoning from Ensemble Research
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Computers and Operations Research
Deciding the financial health of dot-coms using rough sets
Information and Management
Application of a hybrid intelligent decision support model in logistics outsourcing
Computers and Operations Research
Soft computing system for bank performance prediction
Applied Soft Computing
A hybrid financial analysis model for business failure prediction
Expert Systems with Applications: An International Journal
An association-based case reduction technique for case-based reasoning
Information Sciences: 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
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 binary classification method for bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Loss and gain functions for CBR retrieval
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
A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
IEEE Transactions on Knowledge and Data Engineering
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Real-time retrieval for case-based reasoning in interactive multiagent-based simulations
Expert Systems with Applications: An International Journal
A case based reasoning approach on supplier selection in petroleum enterprises
Expert Systems with Applications: An International Journal
A fuzzy case based reasoning approach to value engineering
Expert Systems with Applications: An International Journal
A novel case based reasoning approach to radiotherapy planning
Expert Systems with Applications: An International Journal
Learning fuzzy rules for similarity assessment in case-based reasoning
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
Using ensembles of binary case-based reasoners
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Ensemble based sensing anomaly detection in wireless sensor networks
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
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Case-based reasoning (CBR) holds the unique capability of making predictions as well as suggestions to corporate executives and organizational decision-makers. How to improve its predictive performance is critical. This research aims to explore an ensemble of CBR from multiple case representations as an alternative to traditional approaches, which aims to produce lower errors than its member CBR predictors and independent CBR predictors and produce better performance in business failure prediction (BFP). This method is to base the member CBR predictors on randomly generated feature subsets in order to produce diversity in them. As a result, the CBR ensemble needs not to consider the two difficult/challenging tasks in BFP, i.e., the optimization of a single CBR and the search of optimal single CBR for a specific problem. We statistically validated the results of the CBR ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, and the classical CBR algorithm. The results from Chinese short-term BFP indicate that the CBR ensemble significantly improves predictive ability of CBR.