| 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 | 274 | 2013 |
| ReliefF for multi-label feature selection N Spolaôr, EA Cherman, MC Monard, HD Lee 2013 Brazilian Conference on Intelligent Systems, 6-11, 2013 | 179 | 2013 |
| Multi-label problem transformation methods: a case study EA Cherman, MC Monard, J Metz CLEI Electronic Journal 14 (1), 4-4, 2011 | 122 | 2011 |
| 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 | 105 | 2012 |
| 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 | 62 | 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 Advances in Artificial Intelligence-SBIA 2012: 21th Brazilian Symposium on …, 2012 | 51 | 2012 |
| 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 | 36 | 2019 |
| A simple approach to incorporate label dependency in multi-label classification E Alvares Cherman, J Metz, MC Monard Advances in Soft Computing: 9th Mexican International Conference on …, 2010 | 30 | 2010 |
| Using ReliefF for multi-label feature selection N Spolaôr, EA Cherman, MC Monard Conferencia Latinoamericana de Informática, 960-975, 2011 | 28* | 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, 261-276, 2015 | 26 | 2015 |
| On the estimation of predictive evaluation measure baselines for multi-label learning J Metz, LFD de Abreu, EA Cherman, MC Monard Advances in Artificial Intelligence–IBERAMIA 2012: 13th Ibero-American …, 2012 | 23 | 2012 |
| 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 | 18* | 2016 |
| One-class quantification D dos Reis, A Maletzke, E Cherman, G Batista Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019 | 17 | 2019 |
| Multi-label active learning: key issues and a novel query strategy EA Cherman, Y Papanikolaou, G Tsoumakas, MC Monard Evolving Systems 10, 63-78, 2019 | 16 | 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 | 12 | 2017 |
| Construçao de uma Representaçao Atributo-valor para Extraçao de Conhecimento a partir de Informaçoes Semi-estruturadas de Laudos Médicos DF Honorato, FC Wu, EA Cherman, HD Lee, MC Monard Memorias, 2007 | 12 | 2007 |
| Towards automatic evaluation of asphalt irregularity using smartphone’s sensors VMA Souza, EA Cherman, RG Rossi, RA Souza Advances in Intelligent Data Analysis XVI: 16th International Symposium, IDA …, 2017 | 11 | 2017 |
| 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 | 11 | 2011 |
| On the need of class ratio insensitive drift tests for data streams A Maletzke, D Reis, E Cherman, G Batista Second international workshop on learning with imbalanced domains: theory …, 2018 | 10 | 2018 |
| A systematic review on experimental multi-label learning N Spolaôr, EA Cherman, J Metz, MC Monard | 10 | 2013 |
Maria-Carolina MonardUniversidade de São Paulo - ICMCVerified email at icmc.usp.br
