A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust Classification for Imprecise Environments
Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
DS '98 Proceedings of the First International Conference on Discovery Science
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Cost-Sensitive Decision Trees with Pre-pruning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Cost-Sensitive splitting and selection method for medical decision support system
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A survey of multiple classifier systems as hybrid systems
Information Fusion
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This paper presents a new approach for the cost-sensitive classification problems based on the Boosting ensemble of support vector machines (SVMs). Different from conventional Boosting ensemble learning methods that adjust the distribution of training instances for minimizing the misclassification rate, the presented approach adjusts the training data distribution so as to minimize the expected cost of classification. This approach has been applied successfully in Microcalcification (MC) detection which is a typical cost-sensitive classification problem in breast cancer diagnosis. Its performance is evaluated by means of Receiver Operating Characteristics (ROC) curves and the expected costs of classification. Experimental results have consistently confirmed that the ROC of the SVM ensemble classifier is very close to the curve enveloping the base classifier ROC curves. This characteristic illustrates that the SVM ensemble is able to always improve the performance of the classification. Furthermore, the experimental results demonstrate that the method presented is able to not only increase the area under the ROC curve (AUC) but also minimize the expected classification cost.