By Associate Professor Gustavo Batista

Conference Papers

Matsubara ET; Monard MC; Batista GEAPA, 2005, 'Multi-view semi-supervised learning: An approach to obtain different views from text datasets', in Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005, IOS Press, pp. 97 - 104, IOS Press

Batista GEAPA; Monard MC; Bazzan ALC, 2004, 'Improving rule induction precision for automated annotation by balancing skewed data sets', in International Symposium on Knowledge Exploration in Life Science Informatics, Springer, Berlin, Heidelberg, pp. 20 - 32, Springer, Berlin, Heidelberg

Prati RC; Batista GEAPA; Monard MC, 2004, 'Learning with class skews and small disjuncts', in Brazilian Symposium on Artificial Intelligence, Springer, Berlin, Heidelberg, pp. 296 - 306, Springer, Berlin, Heidelberg

Monard MC; Batista GEAPA, 2003, 'Graphical methods for classifier performance evaluation', in Torres GL; Abe JM; Mucheroni ML; Cruvinel PE (eds.), ADVANCES IN INTELLIGENT SYSTEMS AND ROBOTICS, IOS PRESS, BRAZIL, MARILIA CITY, pp. 59 - 67, presented at 4th Congress of Logic Applied to Technology, BRAZIL, MARILIA CITY, 10 November 2003 - 12 November 2003

Batista GEAPA; Bazan AL; Monard MC, 2003, 'Balancing training data for automated annotation of keywords: a case study', in Proceedings of the Second Brazilian Workshop on Bioinformatics, pp. 35 - 43

Lorena AC; Batista GEAPA; De Carvalho ACPLF; Monard MC, 2002, 'Splice junction recognition using machine learning techniques', in Proceedings of the First Brazilian Workshop on Bioinformatics, Citeseer, pp. 32 - 39, Citeseer

Lorena AC; Batista GEAPA; de Carvalho ACPLF; Monard MC, 2002, 'The influence of noisy patterns on the performance of learning methods in the splice junction recognition problem', in Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on, IEEE, pp. 31 - 36, IEEE

Batista GEAPA; Monard MC, 2001, 'A study of K-nearest neighbour as a model-based method to treat missing data', in Argentine Symposium on Artificial Intelligence

Baranauskas JA; Monard MC; Batista GEAPA, 2000, 'A computational environment for extracting rules from databases', in Management Information Systems, pp. 321 - 330

Theses / Dissertations

BATISTA GE, 2003, Pré-processamento de dados em aprendizado de máquinas supervisionado., Tese (Doutorado)-Instituto de Ciências Matemáticas e de Computaç ao …

Working Papers

Pashamokhtari A; Batista G; Habibi Gharakheili H, 2023, Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks, arXiv, 2301.06695v2, http://dx.doi.org10.48550/arXiv.2301.06695, https://arxiv.org/pdf/2301.06695

Pashamokhtari A; Batista G; Habibi Gharakheili H, 2022, AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks, arXiv, ARTN 102801, http://dx.doi.org10.1016/j.cose.2022.102801, https://arxiv.org/pdf/2203.09792.pdf

Preprints

Nandi M; Shaghaghi A; Sultan NH; Batista G; Zhao RK; Jha S, 2025, Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI, http://dx.doi.org/10.48550/arxiv.2505.10942

Babaria RJ; Lyu M; Batista G; Sivaraman V, 2025, FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets, http://dx.doi.org/10.1145/3727115

Zhang Y; Batista G; Kanhere SS, 2025, Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification, http://dx.doi.org/10.48550/arxiv.2503.20264

A CAR; Chen X; Shaghaghi A; Batista G; Kanhere S, 2025, Predicting IoT Device Vulnerability Fix Times with Survival and Failure Time Models, http://dx.doi.org/10.48550/arxiv.2501.02520

A. CAR; Shaghaghi A; Batista G; Kanhere SS, 2024, Towards Weaknesses and Attack Patterns Prediction for IoT Devices, http://dx.doi.org/10.48550/arxiv.2408.13172

Maroof U; Batista G; Shaghaghi A; Jha S, 2024, Towards Detecting IoT Event Spoofing Attacks Using Time-Series Classification, http://dx.doi.org/10.48550/arxiv.2407.19662

Hamza A; Gharakheili HH; Benson TA; Batista G; Sivaraman V, 2023, Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity, http://dx.doi.org/10.48550/arxiv.2304.04987

Pashamokhtari A; Okui N; Nakahara M; Kubota A; Batista G; Gharakheili HH, 2023, Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks, http://dx.doi.org/10.48550/arxiv.2301.06695

Pashamokhtari A; Batista G; Gharakheili HH, 2022, AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks, http://dx.doi.org/10.48550/arxiv.2203.09792

da Silva LT; Souza VMA; Batista GEAPA, 2021, An Open-Source Tool for Classification Models in Resource-Constrained Hardware, http://dx.doi.org/10.48550/arxiv.2105.05983

Souza VMA; Reis DMD; Maletzke AG; Batista GEAPA, 2020, Challenges in Benchmarking Stream Learning Algorithms with Real-world Data, http://dx.doi.org/10.1007/s10618-020-00698-5

Reis DD; de Souto M; de Sousa E; Batista G, 2020, Quantifying With Only Positive Training Data

Chen Y; Why A; Batista G; Mafra-Neto A; Keogh E, 2014, Flying Insect Classification with Inexpensive Sensors, http://dx.doi.org/10.48550/arxiv.1403.2654

Other

Souza VMA; Silva DF; Gama J; Batista GEAPA, 2015, Nonstationary environments-archive

Chen Y; Keogh E; Hu B; Begum N; Bagnall A; Queen A; Batista G, 2015, The ucr time series classification archive

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