| 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 | 297 | 2013 |
| ReliefF for Multi-label Feature Selection N Spolaor, EA Cherman, MC Monard, HD Lee Intelligent Systems (BRACIS), 2013 Brazilian Conference on, 6-11, 2013 | 207 | 2013 |
| Robotics applications grounded in learning theories on tertiary education: A systematic review N Spolaôr, FBV Benitti Computers & Education 112, 97-107, 2017 | 175 | 2017 |
| A systematic review of multi-label feature selection and a new method based on label construction N Spolaôr, MC Monard, G Tsoumakas, HD Lee Neurocomputing 180, 3-15, 2016 | 107 | 2016 |
| How have robots supported STEM teaching? FBV Benitti, N Spolaôr Robotics in STEM education: Redesigning the learning experience, 103-129, 2017 | 97 | 2017 |
| Analysis of complexity indices for classification problems: cancer gene expression data AC Lorena, IG Costa, N Spolaôr, MCP de Souto Neurocomputing 75 (1), 33-42, 2012 | 86 | 2012 |
| Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease LR Trambaiolli, N Spolaôr, AC Lorena, R Anghinah, JR Sato Clinical Neurophysiology 128 (10), 2058-2067, 2017 | 82 | 2017 |
| A systematic review on content-based video retrieval N Spolaôr, HD Lee, WSR Takaki, LA Ensina, CSR Coy, FC Wu Engineering Applications of Artificial Intelligence 90, 103557, 2020 | 75 | 2020 |
| 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 | 66 | 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 | 52 | 2012 |
| Multi-objective genetic algorithm evaluation in feature selection N Spolaôr, AC Lorena, HD Lee Evolutionary Multi-Criterion Optimization: 6th International Conference, EMO …, 2011 | 46 | 2011 |
| Prototype system for feature extraction, classification and study of medical images JT Oliva, HD Lee, N Spolaôr, CSR Coy, FC Wu Expert Systems with Applications 63, 267-283, 2016 | 43 | 2016 |
| Evaluating Feature Selection Methods for Multi-Label Text Classification N Spolaôr, G Tsoumakas | 40 | 2013 |
| Automatic recommendation of feature selection algorithms based on dataset characteristics ARS Parmezan, HD Lee, N Spolaôr, FC Wu Expert Systems with Applications 185, 115589, 2021 | 33 | 2021 |
| Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines HD Lee, AI Mendes, N Spolaôr, JT Oliva, ARS Parmezan, FC Wu, ... Knowledge-Based Systems 158, 9-24, 2018 | 33 | 2018 |
| 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 | 26 | 2015 |
| Using ReliefF for multi-label feature selection N Spolaôr, EA Cherman, MC Monard Conferencia Latinoamericana de Informática, 960-975, 2011 | 25 | 2011 |
| Complexity measures of supervised classifications tasks: a case study for cancer gene expression data MCP de Souto, AC Lorena, N Spolaôr, IG Costa Neural Networks (IJCNN), The 2010 International Joint Conference on, 1-7, 2010 | 23 | 2010 |
| A systematic review to identify feature selection publications in multi-labeled data N Spolaôr, MC Monard, HD Lee | 21 | 2012 |
| Label Construction for Multi-label Feature Selection N Spolaor, M Monard, G Tsoumakas, H Lee | 21* | |
Everton Alvares ChermanUniversity of São PauloE-mail confirmado em icmc.usp.br
