By Associate Professor Gustavo Batista

Conference Papers

Batista G; Wang X; Keogh E, 2011, 'A Complexity-Invariant Distance Measure for Time Series', in SDM-2011: Proceedings of SIAM International Conference on Data Mining

Batista G; Keogh E; Neto AM; Rowton E, 2011, 'SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining', in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 761 - 764, ACM

Batista GEAPA; Hao Y; Keogh E; Mafra-Neto A, 2011, 'Towards automatic classification on flying insects using inexpensive sensors', in 2011 10th International Conference on Machine Learning and Applications and Workshops, IEEE, pp. 364 - 369, IEEE

Batista GEAPA; Campana B; Keogh E, 2010, 'Classification of Live Moths Combining Texture, Color and Shape Primitives', in Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on, IEEE, pp. 903 - 906, IEEE

Giusti R; Batista GEAPA, 2010, 'Discovering Knowledge Rules with Multi-Objective Evolutionary Computing', in Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on, IEEE, pp. 119 - 124, IEEE

Prati RC; Batista GEAPA; Monard MC, 2009, 'Data mining with imbalanced class distributions: concepts and methods.', in IICAI, pp. 359 - 376

Batista GEAPA; Silva DF, 2009, 'How k-Nearest Neighbor Parameters Affect its Performance', in X Argentine Symposium on Artificial Intelligence

Prati RC; Batista GEAPA; Monard MC, 2008, 'A study with class imbalance and random sampling for a decision tree learning system', in IFIP International Conference on Artificial Intelligence in Theory and Practice, Springer, Boston, MA, pp. 131 - 140, Springer, Boston, MA

Giusti R; Batista GEAPA; Prati RC, 2008, 'Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules', in Hybrid Intelligent Systems, 2008. HIS’08. Eighth International Conference on, IEEE, pp. 537 - 542, IEEE

Matsubara ET; Prati RC; Batista GEAPA; Monard MC, 2008, 'Missing value imputation using a semi-supervised rank aggregation approach', in Brazilian Symposium on Artificial Intelligence, Springer, Berlin, Heidelberg, pp. 217 - 226, Springer, Berlin, Heidelberg

Batista GEAPA; Prati RC; Monard MC, 2005, 'Balancing strategies and class overlapping', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 24 - 35, http://dx.doi.org/10.1007/11552253_3

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

Conference Abstracts

Koo R; Gosman T; Mishra D; Batista G; Gharakheili HH; Seneviratne A, 2026, 'Poster Abstract: Privacy-Generalisation Trade-Offs in Federated Learning for Cross-Room Wi-Fi Sensing', in Proceedings 2026 ACM IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems Posters and Demos Sensys Adjunct 2026, pp. 133 - 134, http://dx.doi.org/10.1109/SenSys-Adjunct71932.2026.00076

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

Azizi S; Okui N; Nakahara M; Kubota A; Batista G; Gharakaheili HH, 2026, Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling, http://dx.doi.org/10.48550/arxiv.2608.15465

Zhang Y; Batista G; Kanhere SS, 2026, Label Shift Estimation With Incremental Prior Update, http://dx.doi.org/10.48550/arxiv.2604.01651

Sivanathan A; Warren D; Mishra D; Ruj S; Fernandes N; Sheng QZ; Tran M; Luo B; Coscia D; Batista G; Gharakaheili HH, 2026, Generalizable IoT Traffic Representations for Cross-Network Device Identification, http://dx.doi.org/10.48550/arxiv.2601.19315

Zhang Y; Batista G; Kanhere SS, 2025, Instance-Wise Monotonic Calibration by Constrained Transformation, http://dx.doi.org/10.48550/arxiv.2507.06516

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/10.48550/arxiv.2504.02174

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

Sivanathan A; Mishra D; Ruj S; Fernandes N; Sheng QZ; Tran M; Luo B; Coscia D; Batista G; Gharakheili HH, 2024, Decoding Behavioral Patterns and Sequence Dynamics in IoT Traffic with Deep Learning, http://dx.doi.org/10.36227/techrxiv.173202912.23807435/v1

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/10.48550/arxiv.2005.00113

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

Ebrahimi Z; Batista G; Deghat M, Aa-Dladmm: An Accelerated Admm-Based Framework for Training Deep Neural Networks, http://dx.doi.org/10.2139/ssrn.4390626

Jiang VW; Batista G; Bain M, From Fair Graphs to Fair Data: A DAG-Based Approach to Mitigating Bias in AI Systems, http://dx.doi.org/10.21203/rs.3.rs-6832455/v1

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

Back to profile page