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. ​

Last update on September 2026.

2026

• A Framework for Evaluating and Benchmarking Concept Drift Detection Methods. V Cerqueira, H M Gomes, M Heyden, B Pfahringer, A Bifet. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2026.

• Adaptive Approaches toward Fully Incremental Prediction Interval for Data Stream Regression. Y Sun, B Pfahringer, H M Gomes, A Bifet. Knowledge and Information Systems (KAIS), 2026.

• Online Decision Trees for Varying Feature Spaces via Gradient-Based Surrogates. K Feng, H M Gomes, X Zhu, Y He. SIAM International Conference on Data Mining (SDM), 2026.

• Hidden-Context Drift Simulation for Realistic Stream Benchmarks. Y Sun, B Pfahringer, H M Gomes, A Bifet. Discovery Sciences, 2026.

2025

• SLEADE: Disagreement-Based Semi-Supervised Learning for Sparsely Labeled Evolving Data Streams. H M Gomes, J Read, M Grzenda, B Pfahringer, A Bifet. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025.

• Machine Learning for Data Streams with CapyMOA. Y Sun, H M Gomes, A Lee, N Gunasekara, G W Cassales, J J Liu, M Heyden, V Cerqueira, M Bahri, Y S Koh, B Pfahringer, A Bifet. European Conference on Machine Learning (ECML PKDD), 2025.

• Smart Adaptive Ensemble Model for Multiclass Imbalanced Nonstationary Data Streams. A S Palli, J Jaafar, M H Md Saad, A A Mokhtar, H M Gomes, A A Soomro, A R Gilal. Scientific Reports (Nature Portfolio), 2025.

• Towards Explainable Drift Detection and Early Retrain in ML-Based Malware Detection Pipelines. J Tripathi, H M Gomes, M Botacin. International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA), 2025.

• Drift-Aware Machine Learning for Operational State Classification in Biogas Dry Reforming. M A Schreiner, R A Escribano, H M Gomes, P de Almeida, L E S de Oliveira. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2025.

• Kolmogorov-Arnold Networks Still Catastrophically Forget but Differently from MLP. A Lee, H M Gomes, Y Zhang, W B Kleijn. AAAI Conference on Artificial Intelligence, 2025

• Gradient Boosted Bagging for Evolving Data Stream Regression. N Gunasekara, B Pfahringer, H M Gomes, A Bifet. Data Mining and Knowledge Discovery, 2025.

• Dynamic Ensemble Member Selection for Data Stream Classification. Y Sun, B Pfahringer, H M Gomes, A Bifet. ACM International Conference on Information and Knowledge Management (CIKM), 2025.

• RMIDDM: An unsupervised and interpretable concept drift detection method for data streams. R Neto, B Alencar, H M Gomes, A Bifet, J Gama, G Cassales, R Rios. Data Mining and Knowledge Discovery, 2025.

• A Semi-Supervised Active Learning Neural Network for Data Streams With Concept Drift. B Jiao, H M Gomes, B Xue, Y Guo, M Zhang. IEEE Computational Intelligence Magazine, 2025.

• Linear Adaptive Filtering for Regression in Data Streams. J Igual, H M Gomes, B Pfahringer, A Bifet. International Journal of Data Science and Analytics, 2025.

• Machine Learning on the Fly: A Hands-On Tutorial for Streaming Data. H M Gomes, N Gunasekara, Y Sun. IEEE International Conference on Data Engineering (IEEE ICDE), 2025

• Towards more realistic evaluations: The impact of label delays in malware detection pipelines. M Botacin, H M Gomes. Computers & Security, 2025

• Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning. Y Sun, N Lim, G W Cassales, H M Gomes, B Pfahringer, A Bifet, A Dwivedi. Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2025

• Edge Machine Learning for Solar Power Forecasting. G W Cassales, I Petri, H M Gomes, O F Rana, A Bifet. International Conference on Future Internet of Things and Cloud (FiCloud), 2025.

2024

• Leveraging Plasticity in Incremental Decision Trees. M Heyden, HM Gomes, E Fouché, B Pfahringer, K Bohm. European Conference on Machine Learning (ECML PKDD), 2024

• Gradient boosted trees for evolving data streams. N Gunasekara, B Pfahringer, H M Gomes, A Bifet. Machine Learning, Springer, 2024.

• Recurrent Concept Drifts on Data Streams. N Gunasekara, B Pfahringer, HM Gomes, A Bifet, Y S Koh. International Joint Conferences on Artificial Intelligence (IJCAI), 2024

• Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. Y S Koh, A Bifet, K Bryan, G Cassales, O Graffeuille, N Lim, P Mourot, D Ning, B Pfahringer, V Vetrova , H M Gomes. International Joint Conferences on Artificial Intelligence (IJCAI), 2024

• Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis Y Sun, H M Gomes, B Pfahringer, A Bifet. Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2024

• Adaptive Prediction Interval for Data Stream Regression. Y Sun, B Pfahringer, A Bifet, H M Gomes. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024

• Practical machine learning for streaming data. H M Gomes, A Bifet. Knowledge Discovery and Data Mining (ACM SIGKDD), 2024

• Machine Learning (In) Security: A Stream of Problems. F Ceschin, H M Gomes, M Botacin, A Bifet, B Pfahringer, L S Oliveira, A Grégio ACM Digital Threats: Research and Practice (DTRAP), 2024.

• SeGDroid: An Android malware detection method based on sensitive function call graph learning. Z Liu, R Wang, N Japkowicz, H M Gomes, B Peng, W Zhang. Expert Systems with Applications, 2024.

• Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review. A S Palli, J Jaafar, A R Gilal, A Alsughayyir, H M Gomes, A Alshanqiti, M Omar. Journal of Information and Communication Technology, 2024.

2023

• Survey on Online Streaming Continual Learning. N Gunasekara, B Pfahringer, HM Gomes, A Bifet. International Joint Conference on Artificial Intelligence (IJCAI), 2023.

• Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning. A Lee, Y Zhang, H M Gomes, A Bifet, B Pfahringer. ACM International Conference on Information and Knowledge Management (CIKM), 2023.

• A decade of biodiversity conservation: insights into corporate social responsibility in an emerging market context E C Mann, H M Gomes, A J Williamson, M C Branco International Journal of Emerging Markets, 2023.

• Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification. A S Palli, J Jaafar, M A Hashmani, H M Gomes, A Alsughayyir, A R Gilal Computers Materials & Continua (CMC), 2023.

• Fast & Furious: Modelling Malware Detection as Evolving Data Streams. F Ceschin, M Botacin, H M Gomes, F Pinagé, L S Oliveira, A Grégio. Expert Systems with Applications, 2023.

2022

• A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams. H M Gomes, M Grzenda, R Mello, J Read, M Huong Le Nguyen, A Bifet. ACM Computing Surveys, 2022.

• SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams. Y Sun, B Pfahringer, H M Gomes, A Bifet. Data Mining and Knowledge Discovery, 2022.

• STUDD: a student–teacher method for unsupervised concept drift detection. V Cerqueira, H M Gomes, A Bifet, L Torgo. Machine Learning, Springer, 2022.

• An Eager Splitting Strategy for Online Decision Trees. C Manapragada, H M Gomes, M Salehi, A Bifet, G Webb. Data Mining and Knowledge Discovery, Springer, 2022.

• LP-ROBIN: Link Prediction in Dynamic Networks exploiting Incremental Node Embedding. E P Barracchia, G Pio, A Bifet, H M Gomes, B Pfahringer, M Ceci. Information Sciences, 2022.

• Multiclass Malware Classification using either Static Opcodes or Dynamic API Calls. R Chanajitt, B Pfahringer, H M Gomes, V Yogarajan. Australasian Joint Conference on Artificial Intelligence, 2022.

• An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams. A S Palli, J Jaafar, H M Gomes, M A Hashmani, A R Gilal. Applied Sciences, 2022.

• Resource-aware edge-based stream analytics. I Petri, I Chirila, H M Gomes, A Bifet, O Rana. IEEE Internet Computing, 2022.

• Adaptive Neural Networks for Online Domain Incremental Continual Learning. N Gunasekara, H M Gomes, A Bifet, B Pfahringer. Discovery Sciences, 2022.

• Should a Social Enterprise Publicly “Toot its Virtuous Horn”? An Artificial Intelligence Approach. E Mann, AJ Williamson, H M Gomes, S Dillon, A Bifet. Academy of Management Proceedings, 2022.

• Adaptive Online Domain Incremental Continual Learning. N Gunasekara, H M Gomes, A Bifet, B Pfahringer. International Conference on Artificial Neural Networks (ICANN), 2022.

• Balancing the Stability Plasticity Dilemma with Online Stability Tuning for Continual Learning. A Lee, Y Zhang, H M Gomes. International Joint Conference on Neural Networks (IJCNN), 2022.

• Online Hyperparameter Optimization for Streaming Neural Networks. N Gunasekara, H M Gomes, B Pfahringer, A Bifet. International Joint Conference on Neural Networks (IJCNN), 2022.

2021

• Learning From Evolving Data Streams Through Ensembles of Random Patches H M Gomes, J Read, A Bifet, R J Durrant Knowledge and Information Systems (KAIS), 2021.

• River: machine learning for streaming data in Python J Montiel, M Halford, S M Mastelini, G Bolmier, R Sourty, R Vaysse, A Zouitine, H M Gomes, J Read, T Abdessalem, A Bifet Journal of Machine Learning (JMLR), 2021.

• Improving the performance of bagging ensembles for data streams through mini-batching. G Cassales, H M Gomes, A Bifet, B Pfahringer, H Senger. Information Sciences, 2021.

• Combining Static and Dynamic Analysis to Improve Machine Learning-based Malware Classification. R Chanajitt, B Pfahringer, H M Gomes. IEEE Data Science and Advanced Analytics (DSAA), 2021.

• Fast and lightweight binary and multi-branch Hoeffding Tree Regressors. SM Mastelini, J Montiel, H M Gomes, A Bifet, B Pfahringer, AC de Carvalho.
  IEEE ICDM Workshops (ICDMW), 2021.

• Data Stream Analysis: Foundations, Major Tasks and Tools. M Bahri, A Bifet, J Gama, H M Gomes, S Maniu. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2021.

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.