Publications
If you are looking for a pdf for one of these publications, take a look at my research gate profile. Another option is my google scholar profile. On a separate note, my lattes CV is available in here, but it is quite outdated.
Last update on December 2020.
2020
Survey on Feature Transformation Techniques for Data Streams. M Bahri, A Bifet, S Maniu, H M Gomes. International Joint Conference on Artificial Intelligence (IJCAI), 2020.
Unsupervised Concept Drift Detection using a Student–Teacher Approach. V Cerqueira, H M Gomes, A Bifet. International Conference on Discovery Science (DS), 2020.
Improving Parallel Performance of Ensemble Learners for Streaming Data Through Data Locality with Mini-Batching. G Cassales, H M Gomes, A Bifet, B Pfahringer, H Senger. The 22nd IEEE International Conference on High Performance Computing and Communications (HPCC), 2020.
No Need to Teach New Tricks to Old Malware: Winning an Evasion Challenge with XOR-based Adversarial Samples. F Ceschin, M Botacin, G Lüders, H M Gomes, L Oliveirea and A Grégio. Reversing and Offensive-oriented Trends Symposium (ROOTS), ACM, 2020.
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams. A Bernardo, H M Gomes, J Montiel, B Pfahringer, A Bifet, and E D Valle. IEEE Big Data, 2020.
Mining Attribute Evolution Rules in Dynamic Attributed Graphs. P Fournier-Viger, G He, J Chun-Wei Lin, H M Gomes. International Conference on Big Data Analytics and Knowledge Discovery (DAWAK), 2020.
On Ensemble Techniques for Data Stream Regression. H M Gomes, J Montiel, S M Mastelini, B Pfahringer, A Bifet. International Joint Conference on Neural Networks (IJCNN), 2020.
Performance measures for evolving predictions under delayed labelling classification. M Grzenda, H M Gomes, A Bifet. International Joint Conference on Neural Networks (IJCNN), 2020.
CS-ARF: Compressed Adaptive Random Forests for Evolving Data Stream Classification. M Bahri, H M Gomes, A Bifet, S Maniu. International Joint Conference on Neural Networks (IJCNN), 2020.
2019
Machine learning for streaming data: state of the art, challenges, and opportunities. H M Gomes, J Read, A Bifet, J P Barddal, J Gama. ACM SIGKDD Explorations Newsletter, 2019, DOI.
Streaming Random Patches for Evolving Data Stream Classification. H M Gomes, J Read, A Bifet. IEEE International Conference on Data Mining (ICDM), 2019. Acceptance rate 9.08%.
Delayed Labelling Evaluation for Data Streams. M Grzenda, H M Gomes, A Bifet. Data Mining and Knowledge Discovery (DAMI), Springer, 2019, DOI
Shallow Security: on the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors. F Ceschin, M Botacin, H M Gomes, L S Oliveira, A Grégio. Reversing and Offensive-oriented Trends Symposium (ROOTS), ACM, 2019.
Network of Experts: Learning from evolving data streams through network-based ensembles. H M Gomes, A Bifet, P Fournier-Viger, J Granatyr, J Read. International Conference on Neural Information Processing (ICONIP), 2019.
Semi-supervised Learning over Streaming Data using MOA. M H L Nguyen, H M Gomes, A Bifet. IEEE Big Data, 2019.
Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. H M Gomes, R Mello, B Pfahringer, A Bifet. IEEE Big Data, 2019.
Inferring Trust Using Personality Aspects Extracted from Texts. J Granatyr, H M Gomes, J M Dias, A M Paiva, M A S Netto Nunes, E E Scalabrin, F Spak. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2019, DOI
Adaptive Random Forests with Resampling for Imbalanced data Streams. L E Boiko Ferreira, H M Gomes, A Bifet, L S Oliveira. International Joint Conference on Neural Networks (IJCNN), 2019.
Boosting decision stumps for dynamic feature selection on data streams. J P Barddal, F Enembreck, H M Gomes, A Bifet, B Pfahringer. Information Systems, 2019
Merit-guided Dynamic Feature Selection Filter for Data Streams. J P Barddal, F Enembreck, H M Gomes, A Bifet, B Pfahringer. Expert Systems with Applications, 2019, DOI
On Social Network-Based Algorithms for Data Stream Clustering. J P Barddal, H M Gomes, F Enembreck. Learning from Data Streams in Evolving Environments (Studies in Big Data), 2019, DOI
2018
Adaptive random forests for data stream regression. H M Gomes, J P Barddal, L E F Boiko, A Bifet. European Symposium on Artificial Neural Networks (ESANN), 2018
An Experimental Perspective on Sampling Methods for Imbalanced Learning from Financial Databases. L E B Boiko, J P Barddal, F Enembreck, H M Gomes. International Joint Conference on Neural Networks (IJCNN), 2018.
Generating action plans for poultry management using artificial neural networks. R Ribeiro, D Casanova, M Teixeira, A Wirth, H M Gomes, A P Borges, F Enembreck. Computers and Electronics in Agriculture, 2018, DOI
2017
A Survey on Ensemble Learning for Data Stream Classification. H M Gomes, J P Barddal, F Enembreck, A Bifet. ACM Computing Surveys 50, 2, Article 23, 2017, DOI
Adaptive random forests for evolving data stream classification. H M Gomes, A Bifet, J Read, J P Barddal, F Enembreck, B Pfahringer, G Holmes, T Abdessalem. Machine Learning, Springer, 2017, DOI
Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques. L E B Ferreira, J P Barddal, H M Gomes, F Enembreck. IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2017
Characteristics of patients who leave without being seen: comparing with those who do not leave. L Zubieta, J R Fernandez-Peña, H M Gomes. Medical Research Archives (MRA), Vol. 5, Issue 4, 2017
2016
Advances in network-based ensemble classifiers for evolving data streams. H M Gomes. ACM Symposium on Applied Computing (SAC), 2016.
A Survey on Feature Drift Adaptation: Definition, Challenges and Future Directions. J P Barddal, H M Gomes, F Enembreck, Pfahringer, B. The Journal of Systems and Software, 2016.
SNCStream+: Extending A High Quality True Anytime Data Stream Clustering Algorithm. J P Barddal, H M Gomes, F Enembreck, Barthes, J P. Information Systems (Oxford), v. 1, p. 1, 2016.
Overcoming Feature Drifts via Dynamic Feature. J P Barddal, H M Gomes, Granatyr, J, Britto, A, F Enembreck. International Conference on Pattern Recognition (ICPR), 2016.
A Benchmark of Classifiers on Feature Drifting Data. J P Barddal, H M Gomes, Britto, A, F Enembreck. International Conference on Pattern Recognition (ICPR), 2016.
On Dynamic Feature Weighting for Feature Drifting Data Streams. J P Barddal, H M Gomes, F Enembreck, Pfahringer, B, Bifet, A. European Conference on Machine Learning (ECML), 2016.
2015
Pairwise Combination of Classifiers for Ensemble Learning on Data Streams. H M Gomes, J P Barddal, F Enembreck. ACM Symposium on Applied Computing (SAC), 2015.
SNCStream: A Scale Network-based Data Stream Clustering Algorithm. J P Barddal, H M Gomes, F Enembreck. ACM Symposium on Applied Computing (SAC), 2015.
A Survey on Feature Drift Adaptation. J P Barddal, F Enembreck , H M Gomes. IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2015.
Applying Ensemble-based Online Learning Techniques on Crime Forecasting. Souza, A J, J P Barddal, Borges, A, F Enembreck , H M Gomes. International Conference on Enterprise Information Systems (ICEIS), 2015.
Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory. J P Barddal, H M Gomes, F Enembreck. International Journal of Natural Computing and Research, 5, article 2, 2015, DOI
On the Discovery of Time Distance Constrained Temporal Association Rules. H M Gomes, D R Carvalho, L Zubieta, J P Barddal, A Malucelli. International Conference on Neural Information Processing (ICONIP), 2015.
Analyzing the Impact of Feature Drifts in Streaming Learning. J P Barddal, H M Gomes, F Enembreck. International Conference on Neural Information Processing (ICONIP), 2015.
AAGEngine: Action Adventure Game Engine. A C Peixoto, H M Gomes. Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames), 2015.
A Complex Network-based Anytime Data Stream Clustering Algorithm. J P Barddal, H M Gomes, F Enembreck. International Conference on Neural Information Processing (ICONIP), 2015.
2014
SAE2: Advances in the Social Adaptive Ensemble. H M Gomes, F Enembreck. ACM Symposium on Applied Computing (SAC), 2014.
SFNClassifier: A Scale-free Social Network Method to Handle Concept Drift. J P Barddal, H M Gomes, F Enembreck. ACM Symposium on Applied Computing (SAC), 2014.
2013
- SAE: Social Adaptive Ensemble classifier for data streams. H M Gomes, F Enembreck. IEEE Symposium on Applied Computational Intelligence and Data Mining (CIDM), 2013.
2011
- A Hybrid data mining method: exploring sequential indicators over association rules. H M Gomes, D R Carvalho. Iberoamerican Journal of Applied Computing, v. 1, p. 40-60, 2011.