Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Classification of weld flaws with imbalanced class data
Expert Systems with Applications: An International Journal
Adaptive Selection of Classifier Ensemble Based on GMDH
FITME '08 Proceedings of the 2008 International Seminar on Future Information Technology and Management Engineering
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
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Combining multiple classifiers combination, sampling techniques, and more appropriate evaluation metrics, we first compare the selection of multiple classifiers combination based on GMDH(S-GMDH) and other classification methods on nine class imbalance data sets; we analyze the change of classification performances with and without using sampling. Then we further do customer churn prediction on 'churn' from the nine data sets. It is concluded that class imbalance has severely affected classification performances of various classifiers, which will surely influence churn prediction. Experiments prove that it is an effective way to improve churn prediction by combining S-GMDH and sampling techniques.