Intervening in problematic data regions to improve machine learning models - Willian, Gregully; Porto, Fabio; Pena, Eduardo H. M. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de Dados, 2025)
Debugging machine learning models is essential to improving their robustness and performance. This work explores a data-driven debugging approach based on problematic data regions—subsets of the data where the model performs poorly compared to others. These regions often reflect problems such as class imbalance, bias, or unfairness. We propose to improve model performance by focusing primarily on the data, using a specialized algorithm to identify problematic regions in the datasets, and then applying targeted interventions. A common cause for poor performance is class imbalance within a problematic region. For such scenarios, we apply data augmentation in a controlled manner, avoiding excessive introduction of synthetic data. Our experiments demonstrate that it is possible to improve model performance by focusing exclusively on problematic data regions rather than the entire dataset.