3 papers
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…
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…
Problem-oriented AutoML in Clustering
Matheus Camilo da Silva, Gabriel Marques Tavares, Eric Medvet +1
The Problem-oriented AutoML in Clustering (PoAC) framework introduces a novel, flexible approach to automating clustering tasks by addressing the shortcomings of traditional AutoML…