2002, Data mining, Data-driven and Measurement

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. This renders measures like classification accuracy meaningless. In their 2002 paper Chawla et al. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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