Towards Meta-Learning for Multi-Target Regression Problems. - Gabriel Jonas Aguiar; Everton José Santana; Saulo Martiello Mastelini; Rafael Gomes Mantovani; Sylvio Barbon Jr. (BRACIS, 2019)

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization. - Gabriel Fillipe Centini Campos; Saulo Martiello Mastelini; Gabriel Jonas Aguiar; Rafael Gomes Mantovani; Leonimer Flavio de Melo; Sylvio Barbon Junior (EURASIP J. Image Video Process., 2019)

A meta-learning approach for selecting image segmentation algorithm. - Gabriel Jonas Aguiar; Rafael Gomes Mantovani; Saulo Martiello Mastelini; André C. P. L. F. de Carvalho; Gabriel F. C. Campos; Sylvio Barbon Junior (Pattern Recognit. Lett., 2019)

Using meta-learning for multi-target regression. - Gabriel Jonas Aguiar; Everton José Santana; André C. P. L. F. de Carvalho; Sylvio Barbon Junior (Inf. Sci., 2022)

Enhancing Concept Drift Detection in Drifting and Imbalanced Data Streams through Meta-Learning. - Gabriel Jonas Aguiar; Alberto Cano 001 (IEEE Big Data, 2023)

An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams. - Gabriel Jonas Aguiar; Alberto Cano 001 (SAC, 2023)

Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners. - Sylvio Barbon; Rodrigo Capobianco Guido; Gabriel Jonas Aguiar; Everton José Santana; Mario Lemes Proença Jr.; Hemant A. Patil (Speech Commun., 2023)

A comprehensive analysis of concept drift locality in data streams. - Gabriel Jonas Aguiar; Alberto Cano 001 (Knowl. Based Syst., 2024)

Dynamic budget allocation for sparsely labeled drifting data streams. - Gabriel Jonas Aguiar; Alberto Cano 001 (Inf. Sci., 2024)

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. - Gabriel Aguiar; Bartosz Krawczyk; Alberto Cano 001 (Mach. Learn., 2024)