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cs.LG2026
Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning
Matheus Camilo da Silva, Gabriel Gustavo Costanzo, Andrea de Lorenzo +1
Many machine learning classification tasks involve imbalanced datasets, which are often subject to over-sampling techniques aimed at improving model performance. However, these tec…
cs.LG2026
Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
Matheus Camilo da Silva, Leonardo Arrighi, Ana Carolina Lorena +1
AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often lev…