C4.5: programs for machine learning
C4.5: programs for machine learning
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
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Does cost-sensitive learning beat sampling for classifying rare classes?
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Decision Support Systems
A rules-based approach for configuring chains of classifiers in real-time stream mining systems
EURASIP Journal on Advances in Signal Processing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
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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This paper examines whether classifier utility can be improved by altering the misclassification cost ratio (the ratio of false positive misclassification costs to false negative misclassification costs) associated with two-class datasets. This is evaluated by varying the cost ratio passed into two cost-sensitive learners and then evaluating the results using the actual (or presumed actual) cost information. Our results indicate that a cost ratio other than the true ratio often maximizes classifier utility. Furthermore, by using a hold out set to identify the "best" cost ratio for learning, we are able to take advantage of this behavior and generate classifiers that outperform the accepted strategy of always using the actual cost information during the learning phase.