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Heitor Murilo Gomes
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2020 – today
- 2025
- [j29]Marcus Botacin, Heitor Murilo Gomes
:
Towards more realistic evaluations: The impact of label delays in malware detection pipelines. Comput. Secur. 148: 104122 (2025) - 2024
- [j28]Fabrício Ceschin
Machine Learning (In) Security: A Stream of Problems. DTRAP 5(1): 9:1-9:32 (2024) - [j27]Zhen Liu
SeGDroid: An Android malware detection method based on sensitive function call graph learning. Expert Syst. Appl. 235: 121125 (2024) - [j26]Nuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes
Gradient boosted trees for evolving data streams. Mach. Learn. 113(5): 3325-3352 (2024) - [c49]Reginaldo Luna
Mini-batching with Fused Training and Testing for Data Streams Processing on the Edge. CF 2024 - [c48]Yun Sing Koh, Albert Bifet, Karin R. Bryan, Guilherme Weigert Cassales, Olivier Graffeuille, Nick Jin Sean Lim, Phil Mourot, Ding Ning, Bernhard Pfahringer, Varvara Vetrova, Heitor Murilo Gomes:
Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. IJCAI 2024: 7314-7322 - [c47]Nuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet, Yun Sing Koh:
Recurrent Concept Drifts on Data Streams. IJCAI 2024: 8029-8037 - [c46]Heitor Murilo Gomes
Practical Machine Learning for Streaming Data. KDD 2024: 6418-6419 - [c45]Yibin Sun
Adaptive Prediction Interval for Data Stream Regression. PAKDD (3) 2024: 130-141 - [c44]Marco Heyden
Leveraging Plasticity in Incremental Decision Trees. ECML/PKDD (5) 2024: 38-54 - [c43]Yibin Sun
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis. PRICAI (5) 2024: 91-97 - [c42]Marcus Botacin
Cross-Regional Malware Detection via Model Distilling and Federated Learning. RAID 2024: 97-113 - [i11]Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer, Albert Bifet:
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis. CoRR abs/2408.16187 (2024) - 2023
- [j25]Heitor Murilo Gomes
A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams. ACM Comput. Surv. 55(4): 75:1-75:42 (2023) - [j24]Fabrício Ceschin
Fast & Furious: On the modelling of malware detection as an evolving data stream. Expert Syst. Appl. 212: 118590 (2023) - [j23]Vítor Cerqueira
STUDD: a student-teacher method for unsupervised concept drift detection. Mach. Learn. 112(11): 4351-4378 (2023) - [j22]Guilherme Weigert Cassales
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing. IEEE Trans. Netw. Serv. Manag. 20(3): 3038-3054 (2023) - [c41]Anton Lee
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning. CIKM 2023: 4038-4042 - [c40]Nuwan Gunasekara
Survey on Online Streaming Continual Learning. IJCAI 2023: 6628-6637 - [i10]Jean Paul Barddal, Heitor Murilo Gomes
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory. CoRR abs/2304.09788 (2023) - [i9]Anton Lee, Yaqian Zhang, Heitor Murilo Gomes
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning. CoRR abs/2310.20052 (2023) - 2022
- [j21]Abdul Sattar Palli
A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis. IEEE Access 10: 118639-118653 (2022) - [j20]Chaitanya Manapragada
An eager splitting strategy for online decision trees in ensembles. Data Min. Knowl. Discov. 36(2): 566-619 (2022) - [j19]Yibin Sun
SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams. Data Min. Knowl. Discov. 36(5): 2006-2032 (2022) - [j18]Ioan Petri
Resource-Aware Edge-Based Stream Analytics. IEEE Internet Comput. 26(4): 79-88 (2022) - [j17]Emanuele Pio Barracchia
LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding. Inf. Sci. 606: 702-721 (2022) - [c39]Rajchada Chanajitt, Bernhard Pfahringer
Multiclass Malware Classification Using Either Static Opcodes or Dynamic API Calls. AI 2022: 427-441 - [c38]Nuwan Gunasekara
Adaptive Neural Networks for Online Domain Incremental Continual Learning. DS 2022: 89-103 - [c37]Rajchada Chanajitt, Bernhard Pfahringer, Heitor Murilo Gomes
A Comparison of Neural Network Architectures for Malware Classification Based on Noriben Operation Sequences. ICANN (1) 2022: 428-440 - [c36]Nuwan Gunasekara
Adaptive Online Domain Incremental Continual Learning. ICANN (1) 2022: 491-502 - [c35]Nuwan Gunasekara
Online Hyperparameter Optimization for Streaming Neural Networks. IJCNN 2022: 1-9 - [c34]Anton Lee
Balancing the Stability-Plasticity Dilemma with Online Stability Tuning for Continual Learning. IJCNN 2022: 1-8 - [e1]Laurence A. F. Park
Data Mining - 20th Australasian Conference, AusDM 2022, Western Sydney, Australia, December 12-15, 2022, Proceedings. Communications in Computer and Information Science 1741, Springer 2022, ISBN 978-981-19-8745-8 [contents] - [i8]Guilherme Weigert Cassales, Heitor Murilo Gomes
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing. CoRR abs/2201.06205 (2022) - [i7]Fabrício Ceschin, Marcus Botacin, Heitor Murilo Gomes
Fast & Furious: Modelling Malware Detection as Evolving Data Streams. CoRR abs/2205.12311 (2022) - 2021
- [j16]Lucas Schmidt, Dalcimar Casanova, Richardson Ribeiro, Marcelo Teixeira
A combined solution for flexible control of poultry houses. Int. J. Comput. Appl. Technol. 67(2/3): 232-243 (2021) - [j15]Guilherme Weigert Cassales
Improving the performance of bagging ensembles for data streams through mini-batching. Inf. Sci. 580: 260-282 (2021) - [j14]Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet:
River: machine learning for streaming data in Python. J. Mach. Learn. Res. 22: 110:1-110:8 (2021) - [j13]Heitor Murilo Gomes
Learning from evolving data streams through ensembles of random patches. Knowl. Inf. Syst. 63(7): 1597-1625 (2021) - [j12]Maroua Bahri
Data stream analysis: Foundations, major tasks and tools. WIREs Data Mining Knowl. Discov. 11(3) (2021) - [c33]Rajchada Chanajitt, Bernhard Pfahringer, Heitor Murilo Gomes
Combining Static and Dynamic Analysis to Improve Machine Learning-based Malware Classification. DSAA 2021: 1-10 - [c32]Saulo Martiello Mastelini, Jacob Montiel, Heitor Murilo Gomes
Fast and lightweight binary and multi-branch Hoeffding Tree Regressors. ICDM (Workshops) 2021: 380-388 - [i6]Vítor Cerqueira, Heitor Murilo Gomes
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection. CoRR abs/2103.00903 (2021) - [i5]Heitor Murilo Gomes
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams. CoRR abs/2106.09170 (2021) - [i4]Guilherme Weigert Cassales, Heitor Murilo Gomes
Improving the performance of bagging ensembles for data streams through mini-batching. CoRR abs/2112.09834 (2021) - 2020
- [j11]Maciej Grzenda
Delayed labelling evaluation for data streams. Data Min. Knowl. Discov. 34(5): 1237-1266 (2020) - [c31]Alessio Bernardo
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams. IEEE BigData 2020: 483-492 - [c30]Philippe Fournier-Viger, Ganghuan He, Jerry Chun-Wei Lin
Mining Attribute Evolution Rules in Dynamic Attributed Graphs. DaWaK 2020: 167-182 - [c29]Vítor Cerqueira
Unsupervised Concept Drift Detection Using a Student-Teacher Approach. DS 2020: 190-204 - [c28]Guilherme Weigert Cassales
Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching. HPCC/DSS/SmartCity 2020: 138-146 - [c27]Maroua Bahri, Albert Bifet, Silviu Maniu, Heitor Murilo Gomes
Survey on Feature Transformation Techniques for Data Streams. IJCAI 2020: 4796-4802 - [c26]Maroua Bahri, Heitor Murilo Gomes
CS-ARF: Compressed Adaptive Random Forests for Evolving Data Stream Classification. IJCNN 2020: 1-8 - [c25]Heitor Murilo Gomes
On Ensemble Techniques for Data Stream Regression. IJCNN 2020: 1-8 - [c24]Maciej Grzenda, Heitor Murilo Gomes
Performance measures for evolving predictions under delayed labelling classification. IJCNN 2020: 1-8 - [i3]Chaitanya Manapragada, Heitor Murilo Gomes
An Eager Splitting Strategy for Online Decision Trees. CoRR abs/2010.10935 (2020) - [i2]Fabricio Ceschin
Machine Learning (In) Security: A Stream of Problems. CoRR abs/2010.16045 (2020) - [i1]Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes
River: machine learning for streaming data in Python. CoRR abs/2012.04740 (2020)
2010 – 2019
- 2019
- [j10]Richardson Ribeiro, Dalcimar Casanova
Generating action plans for poultry management using artificial neural networks. Comput. Electron. Agric. 161: 131-140 (2019) - [j9]Jean Paul Barddal
Merit-guided dynamic feature selection filter for data streams. Expert Syst. Appl. 116: 227-242 (2019) - [j8]Jean Paul Barddal
Boosting decision stumps for dynamic feature selection on data streams. Inf. Syst. 83: 13-29 (2019) - [j7]Heitor Murilo Gomes
Correction to: Adaptive random forests for evolving data stream classification. Mach. Learn. 108(10): 1877-1878 (2019) - [j6]Heitor Murilo Gomes
Machine learning for streaming data: state of the art, challenges, and opportunities. SIGKDD Explor. 21(2): 6-22 (2019) - [c23]Minh-Huong Le Nguyen, Heitor Murilo Gomes
Semi-supervised Learning over Streaming Data using MOA. IEEE BigData 2019: 553-562 - [c22]Heitor Murilo Gomes
Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. IEEE BigData 2019: 761-769 - [c21]Heitor Murilo Gomes
Streaming Random Patches for Evolving Data Stream Classification. ICDM 2019: 240-249 - [c20]Heitor Murilo Gomes
Network of Experts: Learning from Evolving Data Streams Through Network-Based Ensembles. ICONIP (1) 2019: 704-716 - [c19]Luis Eduardo Boiko Ferreira, Heitor Murilo Gomes
Adaptive Random Forests with Resampling for Imbalanced data Streams. IJCNN 2019: 1-6 - [c18]Jones Granatyr, Heitor Murilo Gomes
Inferring Trust Using Personality Aspects Extracted from Texts. SMC 2019: 3840-3846 - 2018
- [c17]Heitor Murilo Gomes, Jean Paul Barddal, Luis Eduardo Boiko Ferreira, Albert Bifet:
Adaptive random forests for data stream regression. ESANN 2018 - [c16]Luis Eduardo Boiko Ferreira, Jean Paul Barddal
An Experimental Perspective on Sampling Methods for Imbalanced Learning From Financial Databases. IJCNN 2018: 1-6 - 2017
- [j5]Heitor Murilo Gomes
A Survey on Ensemble Learning for Data Stream Classification. ACM Comput. Surv. 50(2): 23:1-23:36 (2017) - [j4]Jean Paul Barddal
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. J. Syst. Softw. 127: 278-294 (2017) - [j3]Heitor Murilo Gomes
Adaptive random forests for evolving data stream classification. Mach. Learn. 106(9-10): 1469-1495 (2017) - [c15]Luis Eduardo Boiko Ferreira, Jean Paul Barddal
Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques. ICTAI 2017: 175-181 - 2016
- [j2]Jean Paul Barddal
SNCStream+: Extending a high quality true anytime data stream clustering algorithm. Inf. Syst. 62: 60-73 (2016) - [c14]Jean Paul Barddal
A benchmark of classifiers on feature drifting data streams. ICPR 2016: 2180-2185 - [c13]Jean Paul Barddal
Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning. ICPR 2016: 2186-2191 - [c12]Jean Paul Barddal
On Dynamic Feature Weighting for Feature Drifting Data Streams. ECML/PKDD (2) 2016: 129-144 - [c11]Heitor Murilo Gomes
Advances in network-based ensemble classifiers for evolving data streams: student research abstract. SAC 2016: 958-959 - 2015
- [j1]Jean Paul Barddal, Heitor Murilo Gomes
Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory. Int. J. Nat. Comput. Res. 5(1): 26-41 (2015) - [c10]Anderson José de Souza, André Pinz Borges, Heitor Murilo Gomes
Applying Ensemble-based Online Learning Techniques on Crime Forecasting. ICEIS (1) 2015: 17-24 - [c9]Jean Paul Barddal
Analyzing the Impact of Feature Drifts in Streaming Learning. ICONIP (1) 2015: 21-28 - [c8]Heitor Murilo Gomes
On the Discovery of Time Distance Constrained Temporal Association Rules. ICONIP (2) 2015: 510-519 - [c7]Jean Paul Barddal
A Complex Network-Based Anytime Data Stream Clustering Algorithm. ICONIP (1) 2015: 615-622 - [c6]Jean Paul Barddal
A Survey on Feature Drift Adaptation. ICTAI 2015: 1053-1060 - [c5]Jean Paul Barddal
SNCStream: a social network-based data stream clustering algorithm. SAC 2015: 935-940 - [c4]Heitor Murilo Gomes
Pairwise combination of classifiers for ensemble learning on data streams. SAC 2015: 941-946 - 2014
- [c3]Jean Paul Barddal
SFNClassifier: a scale-free social network method to handle concept drift. SAC 2014: 786-791 - [c2]Heitor Murilo Gomes
SAE2: advances on the social adaptive ensemble classifier for data streams. SAC 2014: 798-804 - 2013
- [c1]Heitor Murilo Gomes
SAE: Social Adaptive Ensemble classifier for data streams. CIDM 2013: 199-206
Coauthor Index
[j28] [j26] [c49] [c48] [c47] [c46] [c45] [c43] [i11] [j25] [j23] [j22] [c41] [c40] [i9] [j20] [j19] [j18] [j17] [c38] [c36] [c35] [i8] [j15] [j14] [j13] [j12] [c32] [i6] [i5] [i4] [j11] [c31] [c29] [c28] [c27] [c26] [c25] [c24] [i3] [i2] [i1] [j9] [j8] [j7] [j6] [c23] [c22] [c21] [c20] [c19] [c17] [j5] [j3] [c12]
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