MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Self-Organizing Maps
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Improving classifier utility by altering the misclassification cost ratio
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Cost-sensitive classifier evaluation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Learning Vector Quantization with Training Data Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
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
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
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Skewed class distribution and non-uniform misclassification cost are pervasive in many real-world domains such as bankruptcy prediction, medical diagnosis, and intrusion detection. Although class imbalance learning and cost-sensitive learning can be manipulated in a unified framework as was illustrated in previous studies, the influence of class distribution on cost-sensitive learning still needs clarification. In this paper, we investigate the effect of cost ratio, imbalance ratio and sample size on classification performance using a real-world French bankruptcy database. The results show that the cost ratio and the level of class imbalance have strong effect on prediction performance. A near-balanced training data set is favorable when a relatively uniform cost ratio is used, whereas a near-natural class distribution is favorable when a highly uneven cost ratio is used.