Geometry and invariance in kernel based methods
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
An Efficient Algorithm for Multi-class Support Vector Machines
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Scaling the kernel function to improve performance of the support vector machine
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Adjusted F-measure and kernel scaling for imbalanced data learning
Information Sciences: an International Journal
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Many critical application domains present issues related to imbalanced learning - classification from imbalanced data. Using conventional techniques produces biased results, as the over-represented class dominates the learning process and tend to naturally attract predictions. As a consequence, the false negative rate may result unacceptable and the chosen classifier unusable. We propose a classification procedure based on Support Vector Machine able to effectively cope with data imbalance. Using a first step approximate solution and then a suitable kernel transformation, we enlarge asymmetrically space around the class boundary, compensating data skewness. Results show that while in case of moderate imbalance the performances are comparable to standard SVM, in case of heavily skewed data the proposed approach outperforms its competitors.