Optimizing SMOTE by Metaheuristics with Neural Network and Decision Tree

Abstract

SMOTE (Synthetic minority over-sampling technique) is a commonly used over-sampling technique to subside the imbalanced dataset problem. Traditionally SMOTE has two key important parameters, one is to control the amount of over-sampling, and the other specifies the area of the nearest neighbors. These two parameters are arbitrarily chosen by user. So there… (More)

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Cite this paper

@article{Li2015OptimizingSB, title={Optimizing SMOTE by Metaheuristics with Neural Network and Decision Tree}, author={Jinyan Li and Simon Fong and Yan Zhuang}, journal={2015 3rd International Symposium on Computational and Business Intelligence (ISCBI)}, year={2015}, pages={26-32} }