Everton Alvares Cherman
Cited by
Cited by
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
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
ReliefF for multi-label feature selection
N Spolaôr, EA Cherman, MC Monard, HD Lee
2013 Brazilian Conference on Intelligent Systems, 6-11, 2013
Multi-label problem transformation methods: a case study
EA Cherman, MC Monard, J Metz
CLEI Electronic Journal 14 (1), 4-4, 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
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
A simple approach to incorporate label dependency in multi-label classification
EA Cherman, J Metz, MC Monard
Mexican International Conference on Artificial Intelligence, 33-43, 2010
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
Using ReliefF for multi-label feature selection
N Spolaor, EA Cherman, MC Monard
Proceedings of Conferencia Latinoamericana de Informatica, 960-975, 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
Construç ao de uma Representaç ao Atributo-valor para Extraç ao de Conhecimento a partir de Informaç oes Semi-estruturadas de Laudos Médicos
DDF Honorato, EA Cherman, HD Lee, MC Monard, F Wu
Anais do Conferencia Latinoamericana de Informática-CLEI. San José-Costa …, 2007
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
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
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
Active learning algorithms for multi-label data
EA Cherman, G Tsoumakas, MC Monard
IFIP International Conference on Artificial Intelligence Applications and …, 2016
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
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
Image processing in mobile devices to classify pressure injuries
CM Tibes, EA Cherman, VMA Souza, YDM Évora, SH Zem-Mascarenhas
Rev Enferm UFPE [Internet] 10 (11), 3840-7, 2016
A systematic review on experimental multi-label learning.
N Spolaôr, EA Cherman, J Metz, MC Monard
São Carlos, SP, Brasil., 2013
A study on the selection of local training sets for hierarchical classification tasks
J Metz, AA Freitas, MC Monard, EA Cherman
ENIA 2011: Encontro Nacional de Inteligncia Artificial, 572-583, 2011
The system can't perform the operation now. Try again later.
Articles 1–20