2001, Forecasting and Knowledge Discovery

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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MAY 20, 2021

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., References. Banko, M., & Brill, E. Quinlan, J.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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