The nature of statistical learning theory
The nature of statistical learning theory
Further results on the margin distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The class imbalance problem: A systematic study
Intelligent Data Analysis
Improving supervised learning for meeting summarization using sampling and regression
Computer Speech and Language
The imbalanced problem in morphological galaxy classification
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Adjusted F-measure and kernel scaling for imbalanced data learning
Information Sciences: an International Journal
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Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for dealing with the class imbalance problem involve modifying the data distribution or modifying the classifier. In this work, we choose to use a combination of both approaches. We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. Then we use a boosting algorithm to get an ensemble classifier that has lower error than a single classifier.We found that this ensemble of SVMs makes an impressive improvement in prediction performance, not only for the majority class, but also for the minority class.