Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets
نویسندگان
چکیده
Learning from imbalanced datasets has drawn more and more attentions from both theoretical and practical aspects. Over-sampling is a popular and simple method for imbalanced learning. In this paper, we show that there is an inherently potential risk associated with the oversampling algorithms in terms of the large margin principle. Then we propose a new synthetic over sampling method, named Margin-guided Synthetic Over-sampling (MSYN), to reduce this risk. The MSYN improves learning with respect to the data distributions guided by the marginbased rule. Empirical study verities the efficacy of MSYN.
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تاریخ انتشار 2011