A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Self-Organizing Maps
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Learning manifolds for bankruptcy analysis
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Accurate prediction of financial distress of companies with machine learning algorithms
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Credit scoring for SME using a manifold supervised learning algorithm
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
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Credit rating is involved in many financial applications to estimate the creditworthiness of corporations or individuals. In addition to building accurate credit rating models, the stability of models is of significant importance to economic performance. In this work we propose a methodology based on learning vector quantization (LVQ) to develop a credit rating model. This model is applied to a French database of private companies over a period of several years. LVQ is trained and calibrated in a supervised way using data from 2006 and then applied to the remaining years. We analyze one year transition matrix and show that the model is capable to create robust and stable classes to rank companies.