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Newton Spolaôr
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2020 – today
- 2024
- [j14]Newton Spolaôr
, Huei Diana Lee, Ana Isabel Mendes, Conceição Veloso Nogueira
Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets. Multim. Tools Appl. 83(9): 27305-27329 (2024) - 2021
- [j13]Antonio Rafael Sabino Parmezan
Automatic recommendation of feature selection algorithms based on dataset characteristics. Expert Syst. Appl. 185: 115589 (2021) - [j12]Newton Spolaôr
A video indexing and retrieval computational prototype based on transcribed speech. Multim. Tools Appl. 80(25): 33971-34017 (2021) - 2020
- [j11]Newton Spolaôr, Huei Diana Lee
A systematic review on content-based video retrieval. Eng. Appl. Artif. Intell. 90: 103557 (2020)
2010 – 2019
- 2019
- [j10]Jefferson Tales Oliva
A computational system based on ontologies to automate the mapping process of medical reports into structured databases. Expert Syst. Appl. 115: 37-56 (2019) - [j9]Thiago Ferreira de Toledo
Web System Prototype based on speech recognition to construct medical reports in Brazilian Portuguese. Int. J. Medical Informatics 121: 39-52 (2019) - 2018
- [j8]Huei Diana Lee
Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines. Knowl. Based Syst. 158: 9-24 (2018) - 2017
- [j7]Newton Spolaôr
Feature Selection via Pareto Multi-objective Genetic Algorithms. Appl. Artif. Intell. 31(9-10): 764-791 (2017) - [j6]Newton Spolaôr
Robotics applications grounded in learning theories on tertiary education: A systematic review. Comput. Educ. 112: 97-107 (2017) - 2016
- [j5]Jefferson Tales Oliva
Prototype system for feature extraction, classification and study of medical images. Expert Syst. Appl. 63: 267-283 (2016) - [j4]Newton Spolaôr
A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing 180: 3-15 (2016) - 2015
- [j3]Newton Spolaôr, Huei Diana Lee
Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations. Int. J. Comput. Intell. Syst. 8(sup2): 3-15 (2015) - [j2]Everton Alvares Cherman, Newton Spolaôr
Lazy Multi-label Learning Algorithms Based on Mutuality Strategies. J. Intell. Robotic Syst. 80(Supplement-1): 261-276 (2015) - [c13]Newton Spolaôr, Maria Carolina Monard, Huei Diana Lee:
Feature Selection for Multi-Label Learning. IJCAI 2015: 4401-4402 - [i1]Jean Metz, Newton Spolaôr, Everton Alvares Cherman, Maria Carolina Monard:
Comparing published multi-label classifier performance measures to the ones obtained by a simple multi-label baseline classifier. CoRR abs/1503.06952 (2015) - 2014
- [b1]Newton Spolaôr:
Feature selection for multi-label learning (Seleção de atributos para aprendizagem multirrótulo). University of São Paulo, Brazil, 2014 - [c12]Newton Spolaôr
Label Construction for Multi-label Feature Selection. BRACIS 2014: 247-252 - [c11]Victor Augusto Moraes Carvalho, Newton Spolaôr
A framework for multi-label exploratory data analysis: ML-EDA. CLEI 2014: 1-12 - [c10]Newton Spolaôr
Evaluating ReliefF-Based Multi-Label Feature Selection Algorithm. IBERAMIA 2014: 194-205 - 2013
- [c9]Newton Spolaôr
ReliefF for Multi-label Feature Selection. BRACIS 2013: 6-11 - [c8]Newton Spolaôr, Grigorios Tsoumakas:
Evaluating Feature Selection Methods for Multi-Label Text Classication. BioASQ@CLEF 2013 - [c7]Jimena Torres Tomás, Newton Spolaôr
A Framework to Generate Synthetic Multi-label Datasets. CLEI Selected Papers 2013: 155-176 - 2012
- [j1]Ana Carolina Lorena
Analysis of complexity indices for classification problems: Cancer gene expression data. Neurocomputing 75(1): 33-42 (2012) - [c6]Newton Spolaôr, Everton Alvares Cherman, Maria Carolina Monard, Huei Diana Lee:
Filter Approach Feature Selection Methods to Support Multi-label Learning Based on ReliefF and Information Gain. SBIA 2012: 72-81 - [c5]Newton Spolaôr
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach. CLEI Selected Papers 2012: 135-151 - 2011
- [c4]Newton Spolaôr
Multi-objective Genetic Algorithm Evaluation in Feature Selection. EMO 2011: 462-476 - 2010
- [c3]Marcílio Carlos Pereira de Souto, Ana Carolina Lorena
Complexity measures of supervised classifications tasks: A case study for cancer gene expression data. IJCNN 2010: 1-7 - [c2]Ana Carolina Lorena
On the Complexity of Gene Marker Selection. SBRN 2010: 85-90 - [c1]Newton Spolaôr
Use of Multiobjective Genetic Algorithms in Feature Selection. SBRN 2010: 146-151
Coauthor Index
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