Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support Vector Data Description
Machine Learning
Classifying imbalanced data using a bagging ensemble variation (BEV)
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
The class imbalance problem: A systematic study
Intelligent Data Analysis
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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As machine learning acquires special attention for real-world problem solving, a growing number of new problems not previously considered have appeared. One of such problems is the imbalance in class distributions, which is said to hinder the performance of traditional error-minimization-based classification algorithms. In this paper we propose an improved rule-based decision boundary for the Support Vector Domain Description that uses an additional nested classification unit to improve the accuracy of the outlier class, hence improving the overall performance of the classifier. Computer simulations show that the proposed strategy, which we have termed Dual Support Vector Domain Description, outperforms related literature approaches in several benchmark instances.