Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing Maps
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Application of LVQ to novelty detection using outlier training data
Pattern Recognition Letters
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
A threshold varying bisection method for cost sensitive learning in neural networks
Expert Systems with Applications: An International Journal
Classification of Unbalanced Medical Data with Weighted Regularized Least Squares
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
A binary classification method for bankruptcy prediction
Expert Systems with Applications: An International Journal
Genetic programming for credit scoring: The case of Egyptian public sector banks
Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Weighted learning vector quantization to cost-sensitive learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A stable credit rating model based on learning vector quantization
Intelligent Data Analysis
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
Bankruptcy trajectory analysis on french companies using self-organizing map
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
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Financial distress prediction is of crucial importance in credit risk analysis with the increasing competition and complexity of credit industry. Although a variety of methods have been applied in this field, there are still some problems remained. The accurate and sensitive prediction in presence of unequal misclassification costs is an important one. Learning vector quantization (LVQ) is a powerful tool to solve financial distress prediction problem as a classification task. In this paper, a cost-sensitive version of LVQ is proposed which incorporates the cost information in the model. Experiments on two real data sets show the proposed approach is effective to improve the predictive capability in cost-sensitive situation.