Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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
Aligning Boundary in Kernel Space for Learning Imbalanced Dataset
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Grouping of TRIZ Inventive Principles to facilitate automatic patent classification
Expert Systems with Applications: An International Journal
Application of distributed SVM architectures in classifying forest data cover types
Computers and Electronics in Agriculture
FLSOM with Different Rates for Classification in Imbalanced Datasets
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Several SVM Ensemble Methods Integrated with Under-Sampling for Imbalanced Data Learning
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
Borderline over-sampling for imbalanced data classification
International Journal of Knowledge Engineering and Soft Data Paradigms
Sample subset optimization for classifying imbalanced biological data
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Identification of Robust Terminal-Area Routes in Convective Weather
Transportation Science
z-SVM: an SVM for improved classification of imbalanced data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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
Neurocomputing
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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Learning from imbalanced datasets is inherently difficult due to lack of information about the minority class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique with an ensemble of SVMs to improve the prediction performance. The integrated sampling technique combines both over-sampling and under-sampling techniques. Through empirical study, we show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers.