2002, Knowledge Discovery and Risk

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

In their 2002 paper Chawla et al. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling. 2002) provide an example that illustrates the modifications. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al.,

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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