A comparison of multi-label feature selection methods using the problem transformation approach N Spolaôr, EA Cherman, MC Monard, HD Lee Electronic notes in theoretical computer science 292, 135-151, 2013 | 213 | 2013 |
ReliefF for multi-label feature selection N Spolaôr, EA Cherman, MC Monard, HD Lee 2013 Brazilian Conference on Intelligent Systems, 6-11, 2013 | 122 | 2013 |
Incorporating label dependency into the binary relevance framework for multi-label classification E Alvares-Cherman, J Metz, MC Monard Expert Systems with Applications 39 (2), 1647-1655, 2012 | 96 | 2012 |
Multi-label problem transformation methods: a case study EA Cherman, MC Monard, J Metz CLEI Electronic Journal 14 (1), 4-4, 2011 | 95 | 2011 |
A framework to generate synthetic multi-label datasets JT Tomás, N Spolaôr, EA Cherman, MC Monard Electronic Notes in Theoretical Computer Science 302, 155-176, 2014 | 50 | 2014 |
Filter approach feature selection methods to support multi-label learning based on relieff and information gain N Spolaôr, EA Cherman, MC Monard, HD Lee Brazilian Symposium on Artificial Intelligence, 72-81, 2012 | 48 | 2012 |
A simple approach to incorporate label dependency in multi-label classification E Alvares Cherman, J Metz, MC Monard Mexican International Conference on Artificial Intelligence, 33-43, 2010 | 30 | 2010 |
Using ReliefF for multi-label feature selection N Spolaor, EA Cherman, MC Monard Conferencia Latinoamericana de Informática, 960-975, 2011 | 26 | 2011 |
Lazy multi-label learning algorithms based on mutuality strategies EA Cherman, N Spolaôr, J Valverde-Rebaza, MC Monard Journal of Intelligent & Robotic Systems 80 (1), 261-276, 2015 | 22 | 2015 |
On the estimation of predictive evaluation measure baselines for multi-label learning J Metz, LFD de Abreu, EA Cherman, MC Monard Ibero-american conference on artificial intelligence, 189-198, 2012 | 22 | 2012 |
Construção de uma representação atributo-valor para extração de conhecimento a partir de informações semi-estruturadas de laudos médicos DF Honorato, FC Wu, EA Cherman, HD Lee, MC Monard Memorias, 2007 | 12 | 2007 |
Dys: a framework for mixture models in quantification A Maletzke, D dos Reis, E Cherman, G Batista Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4552-4560, 2019 | 11 | 2019 |
IMAGE PROCESSING IN MOBILE DEVICES TO CLASSIFY PRESSURE INJURIES. C Mayara Tibes, E Alvares Cherman, V Mourão Alves de Souza, ... Journal of Nursing UFPE/Revista de Enfermagem UFPE 10 (11), 2016 | 11* | 2016 |
Multi-label active learning: key issues and a novel query strategy EA Cherman, Y Papanikolaou, G Tsoumakas, MC Monard Evolving Systems 10 (1), 63-78, 2019 | 10 | 2019 |
Websensors analytics: Learning to sense the real world using web news events RM Marcacini, RG Rossi, BM Nogueira, LV Martins, EA Cherman, ... Anais Estendidos do XXIII Simpósio Brasileiro de Sistemas Multimídia e Web …, 2017 | 10 | 2017 |
Métodos multirrótulo independentes de algoritmo: um estudo de caso EA Cherman, J Metz, MC Monard Proceedings of the XXXVI Conferencia Latinoamericana de Informática (CLEI), 1-14, 2010 | 10 | 2010 |
On the estimation of the number of fuzzy sets for fuzzy rule-based classification systems ME Cintra, MC Monard, EA Cherman, H de Arruda Camargo 2011 11th International Conference on Hybrid Intelligent Systems (HIS), 211-216, 2011 | 9 | 2011 |
Construction of an attribute-value representation for semi-structured medical findings knowledge extraction D de Faveri Honorato, EA Cherman, HD Lee, MC Monard, FC Wu, ... CLEI Electron. J. 11 (2), 2008 | 9 | 2008 |
Towards automatic evaluation of asphalt irregularity using smartphone’s sensors V Souza, EA Cherman, RG Rossi, RA Souza International Symposium on Intelligent Data Analysis, 322-333, 2017 | 8 | 2017 |
Active learning algorithms for multi-label data EA Cherman, G Tsoumakas, MC Monard IFIP International Conference on Artificial Intelligence Applications and …, 2016 | 8 | 2016 |