16 citations · 19 across the 2 of their papers we have counts for
2 papers
cs.LG2025★ 16 cited
Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment
Gissel Velarde, Michael Weichert, Anuj Deshmunkh +4
Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority…
cs.LG2023★ 3 cited
Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection
Gissel Velarde, Anindya Sudhir, Sanjay Deshmane +3
This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evalua…