15 citations · 28 across the 7 of their papers we have counts for
5 papers · 1 filter
-IRT: A New -IRT with Enhanced Discrimination Estimation
Manuel Ferreira-Junior, Jessica T. S. Reinaldo, Telmo M. Silva Filho +2
Item response theory aims to estimate respondent's latent skills from their responses in tests composed of items with different levels of difficulty. Several models of item respons…
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…
Label noise detection under the Noise at Random model with ensemble filters
Kecia G. Moura, Ricardo B. C. Prudêncio, George D. C. Cavalcanti
Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by ad…
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…