1 code implementation • 4 Oct 2022 • Lucas F. F. Cardoso, José de S. Ribeiro, Vitor C. A. Santos, Raíssa L. Silva, Marcelle P. Mota, Ricardo B. C. Prudêncio, Ronnie C. O. Alves
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society.
1 code implementation • 2 Dec 2021 • Kecia G. Moura, Ricardo B. C. Prudêncio, George D. C. Cavalcanti
This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent.
no code implementations • 15 Jul 2021 • Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. Kawasaki Francês, Ricardo B. C. Prudêncio, Ronnie C. O. Alves
The experiments covered by Machine Learning (ML) must consider two important aspects to assess the performance of a model: datasets and algorithms.
1 code implementation • 29 Jul 2020 • Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. K. Francês, Ricardo B. C. Prudêncio, Ronnie C. O. Alves
This work applied IRT to explore the well-known OpenML-CC18 benchmark to identify how suitable it is on the evaluation of classifiers.
1 code implementation • 10 Mar 2019 • Yu Chen, Telmo Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter Flach
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels.