15 citations · 24 across the 5 of their papers we have counts for
5 papers
Standing on the shoulders of giants
Lucas Felipe Ferraro Cardoso, José de Sousa Ribeiro Filho, Vitor Cirilo Araujo Santos +2
Although fundamental to the advancement of Machine Learning, the classic evaluation metrics extracted from the confusion matrix, such as precision and F1, are limited. Such metrics…
Explanation-by-Example Based on Item Response Theory
Lucas F. F. Cardoso, José de S. Ribeiro, Vitor C. A. Santos +4
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have cha…
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
José Ribeiro, Lucas Cardoso, Raíssa Silva +3
In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method…
Data vs classifiers, who wins?
Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. Kawasaki Francês +2
The experiments covered by Machine Learning (ML) must consider two important aspects to assess the performance of a model: datasets and algorithms. Robust benchmarks are needed to…
Decoding machine learning benchmarks
Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. K. Francês +2
Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best…