By Associate Professor Gustavo Batista

Journal articles

Prati RC; Batista GEAPA; Monard MC, 2008, 'Curvas ROC para avaliaç ao de classificadores', Revista IEEE América Latina, 6, pp. 215 - 222

Batista GEAPA; Milaré CR; Prati RC; Monard MC, 2006, 'A Comparison of Methods for Rule Subset Selection Applied to Associative Classification.', Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, 10, pp. 29 - 35

Batista G; Prati R; Monard M, 2005, 'Balancing strategies and class overlapping', Advances in Intelligent Data Analysis VI, pp. 741 - 741

Batista GEAPA; Prati RC; Monard MC, 2004, 'A study of the behavior of several methods for balancing machine learning training data', ACM SIGKDD Explorations Newsletter, 6, pp. 20 - 29

Milaré C; Batista G; de Carvalho A; Monard M, 2004, 'Applying genetic and symbolic learning algorithms to extract rules from artificial neural networks', MICAI 2004: Advances in Artificial Intelligence, pp. 833 - 843

Prati R; Batista G; Monard M, 2004, 'Class imbalances versus class overlapping: an analysis of a learning system behavior', MICAI 2004: Advances in Artificial Intelligence, pp. 312 - 321

Batista GEAPA; Monard MC, 2003, 'An analysis of four missing data treatment methods for supervised learning', Applied Artificial Intelligence, 17, pp. 519 - 533

Batista GEAPA; Monard MC, 2003, 'Descriç ao da arquitetura e do projeto do ambiente computacional DISCOVER LEARNING ENVIRONMENT—DLE', Relatório Técnico do ICMC/USP

Batista GEAPA; Monard MC, 2003, 'Experimental comparison of K-nearest neighbour and mean or mode imputation methods with the internal strategies used by C4. 5 and CN2 to treat missing data', University of Sao Paulo

Batista GEAPA; Monard MC, 2002, 'A Study of K-Nearest Neighbour as an Imputation Method.', HIS, 87, pp. 48 - 48

Batista GEAPA; Monard MC, 2002, 'K-Nearest Neighbour as Imputation Method: Experimental Results', Technical report, ICMC-USP

Monard MC; Batista GEAPA, 2002, 'Learning with Skewed Class Distributions', Advances in Logic, Artificial Intelligence, and Robotics: LAPTEC 2002, 85, pp. 173 - 173

Batista G; Carvalho A; Monard M, 2000, 'Applying one-sided selection to unbalanced datasets', MICAI 2000: Advances in Artificial Intelligence, pp. 315 - 325

Batista GEAPA, 1997, 'Um ambiente de avaliaçao de algoritmos de aprendizado de máquina utilizando exemplos', Dissertaç ao de Mestrado, ICMC-USP

Conference Papers

Tin D; Habibi Gharakheili H; Batista G, 2027, 'Self-supervised Learning for Time Series Classification via Redundancy Reduction and Wavelet-Based Data Augmentation', in Lecture Notes in Computer Science, pp. 65 - 77, http://dx.doi.org/10.1007/978-981-92-1926-1_6

Tsutsui da Silva L; Batista G, 2026, 'ContinualCropBank: Object-Level Replay for Semi-supervised Online Continual Object Detection', in Lecture Notes in Computer Science, pp. 470 - 481, http://dx.doi.org/10.1007/978-981-92-1462-4_37

Zhang Y; Batista G; Kanhere SS, 2026, 'Mosaic: An Accurate and Efficient Kernel-Based Multivariate Time Series Classifier', in Lecture Notes in Computer Science, pp. 301 - 313, http://dx.doi.org/10.1007/978-981-92-1462-4_24

Van Le D; Gupta S; Rashid F; Masood R; Batista G; Seneviratne S; Seneviratne A, 2026, 'Task-Driven Diffusion Denoising for Robust Multimodal Behavioural Biometrics', in Proceedings 2026 22nd International Conference on Distributed Computing in Smart Systems and the Internet of Things Dcoss Iot 2026, pp. 19 - 28, http://dx.doi.org/10.1109/DCOSS-IoT69657.2026.00010

Barazesh Morgani F; Goddard E; Batista G, 2026, 'Using Temporal Features to Improve Accuracy in Multivariate Pattern Analysis (MVPA) of M/EEG Data', in Communications in Computer and Information Science, pp. 182 - 195, http://dx.doi.org/10.1007/978-3-032-16708-8_16

Zhang S; Azizi S; Joshi A; Batista G; Habibi Gharakheili H, 2025, 'Towards Behavior Grammar-Driven IoT Network Traffic Generation using MUD Specifications', in Cpsiotsec 2025 Proceedings of the 7th Joint Workshop on Cps and Iot Security and Privacy Part of ACM Ccs 2025, pp. 98 - 104, http://dx.doi.org/10.1145/3733801.3764203

Azizi S; Calderan FV; Okui N; Nakahara M; Kubota A; Quiles MG; Batista GE; Gharakheili HH, 2025, 'Towards Label Shift Adaptation for Robust IoT Device Identification', in Cpsiotsec 2025 Proceedings of the 7th Joint Workshop on Cps and Iot Security and Privacy Part of ACM Ccs 2025, pp. 105 - 110, http://dx.doi.org/10.1145/3733801.3764204

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', in Proceedings of the ACM Conference on Computer and Communications Security, pp. 473 - 487, http://dx.doi.org/10.1145/3708821.3736188

Donyavi Z; Li F; Zhang Y; Silva DF; Batista GE, 2025, 'Match: A Maximum-Likelihood Approach for Classification under Label Shift', in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 521 - 530, http://dx.doi.org/10.1145/3711896.3737040

Alvarez CAR; Chen X; Shaghaghi A; Batista G; Kanhere S, 2025, 'Predicting IoT Device Vulnerability Fix Times with Survival and Failure Time Models', in Proceedings of 2025 Australasian Computer Science Week Acsw 2025, pp. 55 - 65, http://dx.doi.org/10.1145/3727166.3727189

Babaria RJ; Lyu M; Batista G; Sivaraman V, 2025, 'FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets', in Sigmetrics Abstracts 2025 Abstracts of the 2025 ACM Sigmetrics International Conference on Measurement and Modeling of Computer Systems, pp. 46 - 48, http://dx.doi.org/10.1145/3726854.3727286

Jiang VW; Batista G; Bain M, 2025, 'Charting a Fair Path: FaGGM Fairness-Aware Generative Graphical Models', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 171 - 185, http://dx.doi.org/10.1007/978-981-96-0348-0_13

Zhang Y; Batista G; Kanhere SS, 2025, 'Instance-Wise Monotonic Calibration by Constrained Transformation', in Proceedings of Machine Learning Research, pp. 4920 - 4930

Zhang Y; Batista G; Kanhere SS, 2025, 'Label Shift Estimation With Incremental Prior Update', in 2025 SIAM International Conference on Data Mining Sdm 2025, pp. 134 - 142, http://dx.doi.org/10.1137/1.9781611978520.12

Gil MZ; Hu Z; Lyu M; Batista G; Habibi Gharakheili H, 2024, 'Systematic Mapping and Temporal Reasoning of IoT Cyber Risks using Structured Data', in Asian Internet Engineering Conference Aintec 2024, pp. 18 - 25, http://dx.doi.org/10.1145/3674213.3674216

Azizi S; Okui N; Nakahara M; Kubota A; Batista G; Gharakheili HH, 2024, 'Poster: Understanding and Managing Changes in IoT Device Behaviors for Reliable Network Traffic Inference', in SIGCOMM Posters and Demos 2024 Proceedings of the 2024 SIGCOMM Poster and Demo Sessions Part of SIGCOMM 2024, pp. 25 - 27, http://dx.doi.org/10.1145/3672202.3673723

Wang H; Zhi W; Batista G; Chandra R, 2024, 'Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning', in Proceedings IEEE International Conference on Robotics and Automation, pp. 15068 - 15075, http://dx.doi.org/10.1109/ICRA57147.2024.10609993

Perera Y; Batista G; Hu W; Kanhere S; Jha S, 2024, 'SAfER: Simplified Auto-encoder for (Anomalous) Event Recognition', in Proceedings 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things Dcoss Iot 2024, pp. 229 - 233, http://dx.doi.org/10.1109/DCOSS-IoT61029.2024.00041

Maroof U; Batista G; Shaghaghi A; Jha S, 2024, 'Towards Detecting IoT Event Spoofing Attacks Using Time-Series Classification', in Proceedings Conference on Local Computer Networks LCN, http://dx.doi.org/10.1109/LCN60385.2024.10639633

Rivera A CA; Shaghaghi A; Batista G; Kanhere SS, 2024, 'Towards Weaknesses and Attack Patterns Prediction for IoT Devices', in 2024 17th International Conference on Security of Information and Networks Sin 2024, http://dx.doi.org/10.1109/SIN63213.2024.10871298

Serapião ABS; Donyavi Z; Batista G, 2023, 'Ensembles of Classifiers and Quantifiers with Data Fusion for Quantification Learning', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 3 - 17, http://dx.doi.org/10.1007/978-3-031-45275-8_1

Donyavi Z; Serapio A; Batista G, 2023, 'MC-SQ: A Highly Accurate Ensemble for Multi-class Quantification', in 2023 SIAM International Conference on Data Mining Sdm 2023, pp. 622 - 630, http://dx.doi.org/10.1137/1.9781611977653.ch70

Tin D; Shahpasand M; Gharakheili HH; Batista G, 2022, 'Classifying Time-Series of IoT Flow Activity using Deep Learning and Intransitive Features', in International Conference on Software Knowledge Information Industrial Management and Applications Skima, pp. 192 - 197, http://dx.doi.org/10.1109/SKIMA57145.2022.10029420

Chen B; Bakhshi A; Batista G; Ng B; Chin TJ, 2022, 'Update Compression for Deep Neural Networks on the Edge', in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 3075 - 3085, http://dx.doi.org/10.1109/CVPRW56347.2022.00347

Sharma A; Li J; Mishra D; Batista G; Seneviratne A, 2021, 'Passive WiFi CSI Sensing Based Machine Learning Framework for COVID-Safe Occupancy Monitoring', in 2021 IEEE International Conference on Communications Workshops Icc Workshops 2021 Proceedings, Institute of Electrical and Electronics Engineers (IEEE), ELECTR NETWORK, pp. 1 - 6, presented at 2021 IEEE International Conference on Communications Workshops (ICC Workshops), ELECTR NETWORK, 14 June 2021 - 23 June 2021, http://dx.doi.org/10.1109/ICCWorkshops50388.2021.9473673

Da Silva TP; Parmezan ARS; Batista GEAPA, 2021, 'A Graph-Based Spatial Cross-Validation Approach for Assessing Models Learned with Selected Features to Understand Election Results', in Proceedings 20th IEEE International Conference on Machine Learning and Applications Icmla 2021, pp. 909 - 915, http://dx.doi.org/10.1109/ICMLA52953.2021.00150

Maletzke A; Reis DD; Hassan W; Batista G, 2021, 'Accurately Quantifying under Score Variability', in Proceedings IEEE International Conference on Data Mining Icdm, pp. 1228 - 1233, http://dx.doi.org/10.1109/ICDM51629.2021.00149

Hassan W; Maletzke A; Batista G, 2021, 'Pitfalls in Quantification Assessment', in Ceur Workshop Proceedings

Rebello G; Hu Y; Thilakarathna K; Batista G; Seneviratne A; Duarte OCMB, 2020, 'Melhorando a Acurácia da Detecção de Lavagem de Dinheiro na Rede Bitcoin', in Anais XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020), Sociedade Brasileira de Computação, pp. 728 - 741, presented at Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, http://dx.doi.org/10.5753/sbrc.2020.12321

Jacintho LHM; da Silva TP; Parmezan ARS; de Almeida Prado Alves Batista GE; Batista G, 2020, 'Brazilian Presidential Elections: Analysing Voting Patterns in Time and Space Using a Simple Data Science Pipeline', in Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020), Sociedade Brasileira de Computacao - SB, pp. 217 - 224, presented at Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020), http://dx.doi.org/10.5753/kdmile.2020.11979

Hassan W; Maletzke A; Batista G, 2020, 'Accurately quantifying a billion instances per second', in Proceedings 2020 IEEE 7th International Conference on Data Science and Advanced Analytics Dsaa 2020, pp. 1 - 10, http://dx.doi.org/10.1109/DSAA49011.2020.00012

Parmezan ARS; Silva DF; Batista GEAPA, 2020, 'A COMBINATION OF LOCAL APPROACHES FOR HIERARCHICAL MUSIC GENRE CLASSIFICATION', in Proceedings of the 21st International Society for Music Information Retrieval Conference Ismir 2020, pp. 876 - 883

de Sá JMC; Rossi ALD; Batista GEAPA; Garcia LPF, 2020, 'Algorithm recommendation for data streams', in Proceedings International Conference on Pattern Recognition, pp. 6073 - 6080, http://dx.doi.org/10.1109/ICPR48806.2021.9411923

Maletzke A; Hassan W; dos Reis D; Batista G, 2020, 'The Importance of the Test Set Size in Quantification Assessment', in IJCAI, IJCAI, YOKOHAMA, pp. 2640 - 2646, presented at Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Main track, YOKOHAMA, http://dx.doi.org/10.24963/ijcai.2020/366

Tsutsui Da Silva L; Souza VMA; Batista GEAPA, 2019, 'EmbML Tool: Supporting the use of supervised learning algorithms in low-cost embedded systems', in Proceedings International Conference on Tools with Artificial Intelligence Ictai, pp. 1633 - 1637, http://dx.doi.org/10.1109/ICTAI.2019.00238

Maletzke A; dos Reis D; Cherman E; Batista G, 2019, 'DyS: a Framework for Mixture Models in Quantification', in Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)

Back to profile page