Search Results for author: Yotam Elor

Found 4 papers, 1 papers with code

To SMOTE, or not to SMOTE?

1 code implementation21 Jan 2022 Yotam Elor, Hadar Averbuch-Elor

Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data.

Synthesising Multi-Modal Minority Samples for Tabular Data

no code implementations17 May 2021 Sajad Darabi, Yotam Elor

Furthermore, the superior synthetic data yields better prediction quality in downstream binary classification tasks, as was demonstrated in extensive experiments with 27 publicly available real-world datasets

AE-SMOTE: A Multi-Modal Minority Oversampling Framework

no code implementations1 Jan 2021 Sajad Darabi, Yotam Elor

Real-world binary classification tasks are in many cases unbalanced i. e. the minority class is much smaller than the majority class.

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