Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Soft Computing and Fuzzy Logic
IEEE Software
Case-Based Reasoning in Color Matching
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Deciding Parameter Values with Case-Based Reasoning
Proceedings of the First United Kingdom Workshop on Progress in Case-Based Reasoning
Computers and Operations Research
Expert Systems with Applications: An International Journal
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Forecasting financial condition of Chinese listed companies based on support vector machine
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
Recognizing yield patterns through hybrid applications of machine learning techniques
Information Sciences: an International Journal
Financial distress prediction based on serial combination of multiple classifiers
Expert Systems with Applications: 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
On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Predicting business failure using forward ranking-order case-based reasoning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A case-based reasoning model that uses preference theory functions for credit scoring
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Financial ratio selection for business crisis prediction
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
Computer Methods and Programs in Biomedicine
International Journal of Intelligent Systems in Accounting and Finance Management
Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs
International Journal of Advanced Pervasive and Ubiquitous Computing
Novel feature selection methods to financial distress prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
Financial distress prediction including bankruptcy prediction has called broad attention since 1960s. Various techniques have been employed in this area, ranging from statistical ones such as multiple discriminate analysis (MDA), Logit, etc. to machine learning ones like neural networks (NN), support vector machine (SVM), etc. Case-based reasoning (CBR), which is one of the key methodologies for problem-solving, has not won enough focus in financial distress prediction since 1996. In this study, outranking relations (OR), including strict difference, weak difference, and indifference, between cases on each feature are introduced to build up a new feature-based similarity measure mechanism in the principle of k-nearest neighbors. It is different from traditional distance-based similarity mechanisms and those based on NN, fuzzy set theory, decision tree (DT), etc. Accuracy of the CBR prediction method based on OR, which is called as OR-CBR, is determined directly by such four types of parameters as difference parameter, indifference parameter, veto parameter, and neighbor parameter. It is described in detail that what the model of OR-CBR is from various aspects such as its developed background, formalization of the specific model, and implementation of corresponding algorithm. With three year's real-world data from Chinese listed companies, experimental results indicate that OR-CBR outperforms MDA, Logit, NN, SVM, DT, Basic CBR, and Grey CBR in financial distress prediction, under the assessment of leave-one-out cross-validation and the process of Max normalization. It means that OR-CBR may be a preferred model dealing with financial distress prediction in China.