Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Robust Classification for Imprecise Environments
Machine Learning
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Precision-recall operating characteristic (P-ROC) curves in imprecise environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Exploratory Under-Sampling for Class-Imbalance Learning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Experimental perspectives on learning from imbalanced data
Proceedings of the 24th international conference on Machine learning
The class imbalance problem: A systematic study
Intelligent Data Analysis
Boosting the Performance of Web Spam Detection with Ensemble Under-Sampling Classification
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Facing Imbalanced Classes through Aggregation of Classifiers
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Improving Learner Performance with Data Sampling and Boosting
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
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
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Data preparation techniques for improving rare class prediction
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Computers and Electrical Engineering
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A common problem for data mining and machine learning practitioners is class imbalance. When examples of one class greatly outnumber examples of the other class(es), traditional machine learning algorithms can perform poorly. Random undersampling is a technique that has shown great potential for alleviating the problem of class imbalance. However, undersampling leads to information loss which can hinder classification performance in some cases. To overcome this problem, repetitive undersampling techniques have been proposed. These techniques generate an ensemble of models, each trained on a different, undersampled subset of the training data. In doing so, less information is lost and classification performance is improved. In this study, we evaluate the performance of several repetitive undersampling techniques. To our knowledge, no study has so thoroughly compared repetitive undersampling techniques.