Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration M Kull, MP Nieto, M Kängsepp, T Silva Filho, H Song, P Flach Advances in Neural Information Processing Systems, 12316-12326, 2019 | 343 | 2019 |
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers M Kull, T Silva Filho, P Flach Artificial Intelligence and Statistics, 623-631, 2017 | 205 | 2017 |
Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization TM Silva Filho, BA Pimentel, RMCR Souza, ALI Oliveira Expert Systems with Applications 42 (17-18), 6315-6328, 2015 | 162 | 2015 |
Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration M Kull, TM Silva Filho, P Flach Electronic Journal of Statistics 11 (2), 5052-5080, 2017 | 119 | 2017 |
A parametrized approach for linear regression of interval data LC Souza, RMCR Souza, GJA Amaral, TM Silva Filho Knowledge-Based Systems 131, 149-159, 2017 | 51 | 2017 |
Classifier calibration: a survey on how to assess and improve predicted class probabilities T Silva Filho, H Song, M Perello-Nieto, R Santos-Rodriguez, M Kull, ... Machine Learning 112 (9), 3211-3260, 2023 | 43 | 2023 |
Classifier Calibration: How to assess and improve predicted class probabilities: a survey T Silva Filho, H Song, M Perello-Nieto, R Santos-Rodriguez, M Kull, ... arXiv e-prints, arXiv: 2112.10327, 2021 | 27* | 2021 |
-IRT: A New Item Response Model and its Applications Y Chen, T Silva Filho, RB Prudencio, T Diethe, P Flach The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 26 | 2019 |
Learning Under Concept Drift for Regression—A Systematic Literature Review M Lima, M Neto, T Silva Filho, RAA Fagundes IEEE Access 10, 45410-45429, 2022 | 24 | 2022 |
Background Check: A general technique to build more reliable and versatile classifiers M Perello-Nieto, ES Telmo De Menezes Filho, M Kull, P Flach 2016 IEEE 16th International Conference on Data Mining (ICDM), 1143-1148, 2016 | 19 | 2016 |
An interval prototype classifier based on a parameterized distance applied to breast thermographic images MC Araújo, RMCR Souza, RCF Lima, TM Silva Filho Medical & biological engineering & computing 55 (6), 873-884, 2017 | 12 | 2017 |
Evaluating regression algorithms at the instance level using item response theory JVC Moraes, JTS Reinaldo, M Ferreira-Junior, T Silva Filho, ... Knowledge-Based Systems 240, 108076, 2022 | 8 | 2022 |
A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data TM Silva Filho, RMCR Souza, RBC Prudêncio Neural Networks 80, 19-33, 2016 | 7 | 2016 |
Optimized learning vector quantization classifier with an adaptive euclidean distance RMCR de Souza, T de M. Silva Filho International Conference on Artificial Neural Networks, 799-806, 2009 | 7* | 2009 |
A two-level Item Response Theory model to evaluate speech synthesis and recognition CS Oliveira, JVC Moraes, T Silva Filho, RBC Prudêncio Speech Communication 137, 19-34, 2022 | 5 | 2022 |
Kohonen map-wise regression applied to interval data LC Souza, BA Pimentel, TM Silva Filho, RMCR de Souza Knowledge-Based Systems 224, 107091, 2021 | 5 | 2021 |
Fuzzy learning vector quantization approaches for interval data TM e Silva Filho, RMCR Souza 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8, 2013 | 5 | 2013 |
MaasPenn radiomics reproducibility score: A novel quantitative measure for evaluating the reproducibility of CT-based handcrafted radiomic features A Ibrahim, B Barufaldi, T Refaee, TM Silva Filho, RJ Acciavatti, ... Cancers 14 (7), 1599, 2022 | 4 | 2022 |
Item Response Theory for Evaluating Regression Algorithms J Moraes, J Reinaldo, TM Silva Filho, RBC Prudêncio The International Joint Conference on Neural Networks (IJCNN 2020), Forthcoming, 2020 | 4 | 2020 |
Explaining Learning Performance with Local Performance Regions and Maximally Relevant Meta-Rules RBC Prudêncio, TM Silva Filho Brazilian Conference on Intelligent Systems, 550-564, 2022 | 3 | 2022 |