4 papers
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades +1
Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employ…
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…
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest
Matteo Ceschin, Leonardo Arrighi, Luca Longo +1
The need to explain predictive models is well-established in modern machine learning. However, beyond model interpretability, understanding pre-processing methods is equally essent…