4 citations · 4 across the 3 of their papers we have counts for
4 papers
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Tabular learning tasks often suffer from class imbalance, which is often addressed via weighting-based strategies. In this work, we compare the naive approach (unweighted) with the…
OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data
Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou +3
Although neural-based machine learning models have received a lot of attention recently, tree-based models such as gradient boosting are competitive for tabular data and therefore…
Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier +1
This study investigates rare event detection on tabular data within binary classification. Standard techniques to handle class imbalance include SMOTE, which generates synthetic sa…
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In thi…