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 On a separate note, my lattes CV is available in here, but it is quite outdated.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 September 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.

• 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.