The nature of statistical learning theory
The nature of statistical learning theory
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
One-class svms for document classification
The Journal of Machine Learning Research
A robust minimax approach to classification
The Journal of Machine Learning Research
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
A Criterion for Learning the Data-Dependent Kernel for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Margin calibration in SVM class-imbalanced learning
Neurocomputing
An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
Expert Systems with Applications: An International Journal
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Partially linear support vector machines applied to the prediction of mine slope movements
Mathematical and Computer Modelling: An International Journal
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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As a kernel-based method, whether the selected kernel matches the data determines the performance of support vector machine. Conventional support vector classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority class which is the less important class. In this paper, we propose a weighted maximum margin criterion to optimize the data-dependent kernel, which makes the minority class more clustered in the induced feature space. We train support vector classification with the optimal kernel. The experimental results on nine benchmark data sets indicate the effectiveness of the proposed algorithm for imbalanced data classification problems.