Publications

My publications are also listed in my Google Scholar page.

Pre-prints

• Reis, D., de Souto, M., de Sousa, E., Batista, G., 2020. Quantifying with only positive training data. arXiv preprint arXiv:2004.10356. (https://arxiv.org/pdf/2004.10356)

• Pashamokhtari, A., Okui, N., Nakahara, M., Kubota, A., Batista, G. and Gharakheili, H.H., 2023. Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks. arXiv preprint arXiv:2301.06695. (https://arxiv.org/pdf/2301.06695)

• Hamza, A., Gharakheili, H.H., Benson, T.A., Batista, G. and Sivaraman, V., 2023. Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity. arXiv preprint arXiv:2304.04987. (https://arxiv.org/pdf/2304.04987)

• Wang, H., Zhi, W., Batista, G. and Chandra, R., 2023. Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning. arXiv preprint arXiv:2309.09021. (https://arxiv.org/pdf/2309.09021https://arxiv.org/pdf/2304.04987)

Refereed Journal Articles

• Pashamokhtari, A., Okui, N., Nakahara, M., Kubota, A., Batista, G. and Gharakheili, H.H., 2023. Dynamic Inference from IoT Traffic Flows under Concept Drifts in Residential ISP Networks. IEEE Internet of Things Journal. (https://ieeexplore.ieee.org/abstract/document/10093927/)

• Pashamokhtari, A., Batista, G. and Gharakheili, H.H., 2023. Efficient IoT Traffic Inference: from Multi-View Classification to Progressive Monitoring. ACM Transactions on Internet of Things. (https://dl.acm.org/doi/pdf/10.1145/3625306)

• Pashamokhtari, A., Batista, G. and Gharakheili, H.H., 2022. AdIoTack: Quantifying and refining resilience of decision tree ensemble inference models against adversarial volumetric attacks on IoT networks. Computers & Security, 120, p.102801. (https://www.sciencedirect.com/science/article/pii/S016740482200195X)

• Parmezan, A., Souza, V., Batista, G., 2022. Time Series Prediction via Similarity Search: Exploring Invariances, Distance Measures and Ensemble Functions. IEEE Access, 10, pp.78022-78043, IEEE. (https://ieeexplore.ieee.org/iel7/6287639/9668973/09834330.pdf)

• Parmezan, A., Souza, V., Seth, A., Žliobaitė, I., Batista, G., 2022. Hierarchical classification of pollinating flying insects under changing environments. Ecological Informatics, 70, pp.101751, Elsevier. (https://www.sciencedirect.com/science/article/pii/S1574954122002011)

• Li, J., Sharma, A., Mishra, D., Batista, G., Seneviratne, A., 2021. COVID-safe spatial occupancy monitoring using OFDM-based features and passive WiFi samples. ACM Transactions on Management Information Systems (TMIS), 12(4), pp.Jan-24, ACM New York, NY. (https://dl.acm.org/doi/abs/10.1145/3472668)

• da Silva, L., Souza, V., Batista, G., 2021. An Open-Source Tool for Classification Models in Resource-Constrained Hardware. IEEE Sensors Journal, 22(1), pp.544-554, IEEE. (https://arxiv.org/pdf/2105.05983)

• Parmezan, A., Souza, V., Žliobaitė, I., Batista, G., 2021. Changes in the wing-beat frequency of bees and wasps depending on environmental conditions: a study with optical sensors. Apidologie, 52(4), pp.731-748, Springer Paris. (https://hal.archives-ouvertes.fr/hal-03693758/document)

• De Nadai, B., Maletzke, A., Corbi, J., Batista, G., Reiskind, M., 2021. The impact of body size on Aedes [Stegomyia] aegypti wingbeat frequency: implications for mosquito identification. Medical and Veterinary Entomology, 35(4), pp.617-624, Blackwell Publishing Ltd Oxford, UK. (https://resjournals.onlinelibrary.wiley.com/doi/abs/10.1111/mve.12540)

• Jacintho, L., Silva, T., Parmezan, A., Batista, G., 2021. Analysing spatio-temporal voting patterns in brazilian elections through a simple data science pipeline. Journal of Information and Data Management, pp.Jan-16. (https://sol.sbc.org.br/journals/index.php/jidm/article/download/1932/1773/7062)

• Souza, V., Reis, D., Maletzke, A., Batista, G., 2020. Challenges in Benchmarking Stream Learning Algorithms with Real-world Data. Data Mining and Knowledge Discovery, 34, pp.1805–1858. (https://arxiv.org/pdf/2005.00113)

• Silva, D., Yeh, C., Zhu, Y., Batista, G., Keogh, E., 2019. Fast similarity matrix profile for music analysis and exploration. IEEE Transactions on Multimedia, 21(1), pp.29-38, IEEE. (https://ieeexplore.ieee.org/ielaam/6046/8584144/8392419-aam.pdf)

• Parmezan, A., Souza, V., Batista, G., 2019. Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information sciences, 484, pp.302-337, Elsevier. https://www.researchgate.net/profile/Antonio-Parmezan/publication/330742498_Evaluation_of_statistical_and_machine_learning_models_for_time_series_prediction_Identifying_the_state-of-the-art_and_the_best_conditions_for_the_use_of_each_model/links/5fbc07eda6fdcc6cc65e0d18/Evaluation-of-statistical-and-machine-learning-models-for-time-series-prediction-Identifying-the-state-of-the-art-and-the-best-conditions-for-the-use-of-each-model.pdf)

• Silva, D., Giusti, R., Keogh, E., Batista, G., 2018. Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, 32(4), pp.988-1016, Springer US. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/323703457_Speeding_up_similarity_search_under_dynamic_time_warping_by_pruning_unpromising_alignments/links/5bb1295792851ca9ed331947/Speeding-up-similarity-search-under-dynamic-time-warping-by-pruning-unpromising-alignments.pdf)

• Maletzke, A., dos Reis, D., Batista, G., 2018. Combining instance selection and self-training to improve data stream quantification. Journal of the Brazilian Computer Society, 24(1), pp.Jan-17, SpringerOpen. (https://journal-bcs.springeropen.com/articles/10.1186/s13173-018-0076-0)

• Silva, D., Souza, V., Ellis, D., Keogh, E., Batista, G., 2015. Exploring low cost laser sensors to identify flying insect species. Journal of Intelligent & Robotic Systems, 80(1), pp.313-330, Springer Netherlands. (https://www.academia.edu/download/75028329/Exploring_Low_Cost_Laser_Sensors_to_Iden20211122-17797-1mdnekz.pdf)

• Prati, R., Batista, G., Silva, D., 2015. Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowledge and Information Systems, 45(1), pp.247-270, Springer London. (https://www.academia.edu/download/47025564/Class_imbalance_revisited_a_new_experime20160705-32583-1p9uxnv.pdf)

• Chen, Y., Why, A., Batista, G., Mafra-Neto, A., Keogh, E., 2014. Flying insect detection and classification with inexpensive sensors. JoVE (Journal of Visualized Experiments), pp.e52111. (https://www.jove.com/t/52111/flying-insect-detection-and-classification-with-inexpensive-sensors)

• Chen, Y., Why, A., Batista, G., Mafra-Neto, A., Keogh, E., 2014. Flying insect classification with inexpensive sensors. Journal of insect behavior, 27(5), pp.657-677, Springer US. (https://arxiv.org/pdf/1403.2654)

• Batista, G., Keogh, E., Tataw, O., De Souza, V., 2014. CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28(3), pp.634-669, Springer US. (https://www.researchgate.net/profile/Vinicius-Alves-De-Souza/publication/254861501_CID_An_efficient_complexity-invariant_distance_for_time_series/links/0deec51fe65bc20dfa000000/CID-An-efficient-complexity-invariant-distance-for-time-series.pdf)

• Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E., 2013. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(3), pp.Jan-31, ACM New York, NY, USA. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6790126/)

• Silva, D., de Souza, V., Batista, G., 2013. A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English. Acta Scientiarum. Technology, 35(4), pp.621-628. (https://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/download/19825/pdf/)

• Milaré, C., Batista, G., Carvalho, A., 2011. A hybrid approach to learn with imbalanced classes using evolutionary algorithms. Logic Journal of IGPL, 19(2), pp.293, Oxford Univ Press. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.741.685&rep=rep1&type=pdf)

• Prati, R., Batista, G., Monard, M., 2010. A survey on graphical methods for classification predictive performance evaluation. Knowledge and Data Engineering, IEEE Transactions on, pp.1-Jan, IEEE. (https://www.academia.edu/download/47025577/A_Survey_on_Graphical_Methods_for_Classi20160705-22587-dl91a3.pdf)

• Milaré, C., Batista, G., de Carvalho, A., 2010. A Study of the Influence of Rule Measures in Classifiers Induced by Evolutionary Algorithms.. IEEE Intell. Informatics Bull., 11(1), pp.Aug-13. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.3606&rep=rep1&type=pdf)

• Prati, R., Batista, G., Monard, M., 2008. Curvas ROC para avaliação de classificadores. Revista IEEE América Latina, 6(2), pp.215-222. (https://www.academia.edu/download/48255880/Evaluating_Classifiers_Using_ROC_Curves20160823-31425-1okt5jy.pdf)

• Prati, R., Batista, G., Monard, M., 2008. A hybrid wrapper/filter approach for feature subset selection. Electronic Journal of SADIO (EJS), 8, pp.Dec-24. (https://publicaciones.sadio.org.ar/index.php/EJS/article/download/96/87)

• Batista, G., Milaré, C., Prati, R., Monard, M., 2006. A Comparison of Methods for Rule Subset Selection Applied to Associative Classification.. Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, 10(32), pp.29-35. (https://www.redalyc.org/pdf/925/92503205.pdf)

• Batista, G., Prati, R., Monard, M., 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), pp.20-29, ACM. (http://www.kdd.org/exploration_files/batista.pdf)

• Batista, G., Monard, M., 2003. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5-Jun), pp.519-533, Taylor & Francis. (https://www.tandfonline.com/doi/pdf/10.1080/713827181)

Refereed Conference Papers

• Serapião, A.B., Donyavi, Z. and Batista, G., 2023. Ensembles of Classifiers and Quantifiers with Data Fusion for Quantification Learning. In International Conference on Discovery Science (pp. 3-17). Cham: Springer Nature Switzerland. (https://link.springer.com/chapter/10.1007/978-3-031-45275-8_1)

• Donyavi, Z., Serapio, A. and Batista, G., 2023. MC-SQ: A Highly Accurate Ensemble for Multi-class Quantification. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) (pp. 622-630). Society for Industrial and Applied Mathematics. (https://epubs.siam.org/doi/pdf/10.1137/1.9781611977653.ch70)

• Tin, D., Shahpasand, M., Gharakheili, H.H. and Batista, G., 2022. Classifying Time-Series of IoT Flow Activity using Deep Learning and Intransitive Features. In 2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 192-197). IEEE. (https://ieeexplore.ieee.org/abstract/document/10029420/)

• da Silva, T.P., Parmezan, A.R. and Batista, G., 2022, Geographic Context-Based Stacking Learning for Election Prediction from Socio-economic Data. In Brazilian Conference on Intelligent Systems (pp. 641-656). Cham: Springer International Publishing. (https://www.researchgate.net/profile/Tiago-Pinho-Da-Silva-2/publication/365489998_Geographic_Context-Based_Stacking_Learning_for_Election_Prediction_from_Socio-Economic_Data/links/6377716b1766b34c54363e1d/Geographic-Context-Based-Stacking-Learning-for-Election-Prediction-from-Socio-Economic-Data.pdf)

• Chen, B., Bakhshi, A., Batista, G., Ng, B., Chin, T., 2022. Update Compression for Deep Neural Networks on the Edge. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.3076-3086. (https://openaccess.thecvf.com/content/CVPR2022W/MobileAI/papers/Chen_Update_Compression_for_Deep_Neural_Networks_on_the_Edge_CVPRW_2022_paper.pdf)

• de Sá, J., Rossi, A., Batista, G., Garcia, L., 2021. Algorithm Recommendation for Data Streams. 2020 25th International Conference on Pattern Recognition (ICPR), pp.6073-6080, IEEE. (https://recipp.ipp.pt/bitstream/10400.22/18698/1/ART_LSA_Malheiro_2021.pdf)

• Sharma, A., Li, J., Mishra, D., Batista, G., Seneviratne, A., 2021. Passive WiFi CSI sensing based machine learning framework for COVID-Safe occupancy monitoring. 2021 IEEE International Conference on Communications Workshops (ICC Workshops), pp.1-Jun, IEEE. (https://ieeexplore.ieee.org/abstract/document/9473673/)

• Hassan, W., Maletzke, A., Batista, G., 2021. Pitfalls in Quantification Assessment. First International Workshop on Learning to Quantify: Methods and Applications (LQ 2021)., pp.1-Oct. (https://www.researchgate.net/profile/Andre-Maletzke/publication/357683526_Pitfalls_in_Quantification_Assessment/links/61da38f2b8305f7c4b30ef97/Pitfalls-in-Quantification-Assessment.pdf

• Maletzke, A., Dos Reis, D., Hassan, W., Batista, G., 2021. Accurately Quantifying under Score Variability. 2021 IEEE International Conference on Data Mining (ICDM), pp.1228-1233, IEEE. (https://ieeexplore.ieee.org/abstract/document/9679104/)

• Da Silva, T., Parmezan, A., Batista, G., 2021. A Graph-Based Spatial Cross-Validation Approach for Assessing Models Learned with Selected Features to Understand Election Results. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.909-915, IEEE. (https://www.researchgate.net/profile/Tiago-Pinho-Da-Silva-2/publication/358151627_A_Graph-Based_Spatial_Cross-Validation_Approach_for_Assessing_Models_Learned_with_Selected_Features_to_Understand_Election_Results/links/61f94f5111a1090a79c7a9a1/A-Graph-Based-Spatial-Cross-Validation-Approach-for-Assessing-Models-Learned-with-Selected-Features-to-Understand-Election-Results.pdf)

• Maletzke, A., Hassan, W., dos Reis, D., Batista, G., 2020. The Importance of the Test Set Size in Quantification Assessment. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}}, pp.2640–2646. (https://www.ijcai.org/Proceedings/2020/0366.pdf)

• Rebello, G., Hu, Y., Thilakarathna, K., Batista, G., Senenviratine, A., Duarte, O., 2020. Melhorando a Acurácia da Detecção de Lavagem de Dinheiro na Rede Bitcoin. SBRC. (https://sol.sbc.org.br/index.php/sbrc/article/download/12321/12186)

• Parmezan, A., Silva, D., Batista, G., 2020. A Combination of Local Approaches for Hierarchical Music Genre Classification. International Society for Music Information Retrieval Conference, pp.740-747. (https://program.ismir2020.net/static/final_papers/324.pdf)

• Jacintho, L., da Silva, T., Parmezan, A., Batista, G., 2020. Brazilian Presidential Elections: Analysing Voting Patterns in Time and Space Using a Simple Data Science Pipeline. Anais do VIII Symposium on Knowledge Discovery, Mining and Learning, pp.217-224, SBC. (https://repositorio.usp.br/directbitstream/1d0ad5d8-0daf-4743-b28a-5c53f7dbeca3/3008380.pdf)

• Hassan, W., Maletzke, A., Batista, G., 2020. Accurately quantifying a billion instances per second. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp.1-Oct, IEEE. (https://www.researchgate.net/profile/Andre-Maletzke/publication/344518107_Accurately_Quantifying_a_Billion_Instances_per_Second/links/5f846eb1458515b7cf7a2f9f/Accurately-Quantifying-a-Billion-Instances-per-Second.pdf)

• da Silva, L., Souza, V., Batista, G., 2019. Embml tool: Supporting the use of supervised learning algorithms in low-cost embedded systems. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp.1633-1637, IEEE. (https://www.researchgate.net/profile/Vinicius-Alves-De-Souza/publication/337168440_EmbML_Tool_supporting_the_use_of_supervised_learning_algorithms_in_low-cost_embedded_systems/links/5dc98967299bf1a47b2f97fa/EmbML-Tool-supporting-the-use-of-supervised-learning-algorithms-in-low-cost-embedded-systems.pdf)

• Maletzke, A., dos Reis, D., Cherman, E., Batista, G., 2019. DyS: a Framework for Mixture Models in Quantification. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). (https://ojs.aaai.org/index.php/AAAI/article/download/4376/4254)

• Reis, D., Maletzke, A., Cherman, E., Batista, G., 2018. One-class quantification. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.273-289, Springer, Cham. (http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/125.pdf)

• Silva, D., Batista, G., 2018. Elastic time series motifs and discords. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.237-242, IEEE. (https://ieeexplore.ieee.org/abstract/document/8614067/)

• Souza, V., Pinho, T., Batista, G., 2018. Evaluating stream classifiers with delayed labels information. 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp.408-413, IEEE. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/328465613_Evaluating_Stream_Classifiers_with_Delayed_Labels_Information/links/5bcf777f92851c1816bb3b69/Evaluating-Stream-Classifiers-with-Delayed-Labels-Information.pdf)

• Maletzke, A., dos Reis, D., Cherman, E., Batista, G., 2018. On the Need of Class Ratio Insensitive Drift Tests for Data Streams. Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp.110-124. (http://proceedings.mlr.press/v94/maletzke18a/maletzke18a.pdf)

• Moreira dos Reis, D., Maletzke, A., Silva, D., Batista, G., 2018. Classifying and counting with recurrent contexts. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.1983-1992. (https://dl.acm.org/doi/pdf/10.1145/3219819.3220059)

• dos Reis, D., Maletzke, A., Batista, G., 2018. Unsupervised context switch for classification tasks on data streams with recurrent concepts. Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp.518-524. (https://dl.acm.org/doi/pdf/10.1145/3167132.3167189)

• Souza, V., Rossi, R., Batista, G., Rezende, S., 2017. Unsupervised active learning techniques for labeling training sets: An experimental evaluation on sequential data. Intelligent Data Analysis, 21(5), pp.1061-1095, IOS Press. (https://www.researchgate.net/profile/Vinicius-Alves-De-Souza/publication/320461561_Unsupervised_active_learning_techniques_for_labeling_training_sets_An_experimental_evaluation_on_sequential_data/links/5a856e30a6fdcc201b9f91af/Unsupervised-active-learning-techniques-for-labeling-training-sets-An-experimental-evaluation-on-sequential-data.pdf)

• Maletzke, A., dos Reis, D., Batista, G., 2017. Quantification in data streams: Initial results. 2017 Brazilian Conference on Intelligent Systems (BRACIS), pp.43-48, IEEE. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/322322172_Quantification_in_Data_Streams_Initial_Results/links/5bb129e045851574f7f39240/Quantification-in-Data-Streams-Initial-Results.pdf)

• dos Reis, D., Flach, P., Matwin, S., Batista, G., 2016. Fast unsupervised online drift detection using incremental kolmogorov-smirnov test. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1545-1554. (https://www.kdd.org/kdd2016/papers/files/rpp0427-dos-reisA.pdf)

• Silva, D., Batista, G., Keogh, E., 2016. Prefix and suffix invariant dynamic time warping. 2016 IEEE 16th International Conference on Data Mining (ICDM), pp.1209-1214, IEEE. (https://www.cs.ucr.edu/~eamonn/psi_DTW_10pages.pdf)

• Silva, D., Yeh, C., Batista, G., Keogh, E., 2016. SiMPle: Assessing Music Similarity Using Subsequences Joins.. ISMIR, pp.23-29. (https://www.cs.ucr.edu/~eamonn/MP_Music_ISMIR.pdf)

• Silva, D., Batista, G., 2016. Speeding up all-pairwise dynamic time warping matrix calculation. Proceedings of the 2016 SIAM International Conference on Data Mining, pp.837-845, Society for Industrial and Applied Mathematics. (https://epubs.siam.org/doi/pdf/10.1137/1.9781611974348.94)

• Silva, D., Batista, G., Keogh, E., 2016. On the effect of endpoints on dynamic time warping. SIGKDD Workshop on Mining and Learning from Time Series II, San Francisco, CA. Association for Computing Machinery-ACM. (https://kdd-milets.github.io/milets2016/paper/MiLeTS_2016_paper_7.pdf)

• Giusti, R., Silva, D., Batista, G., 2016. Improved time series classification with representation diversity and svm. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.1-Jun, IEEE. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/313450924_Improved_Time_Series_Classification_with_Representation_Diversity_and_SVM/links/5bb12b5c45851574f7f39280/Improved-Time-Series-Classification-with-Representation-Diversity-and-SVM.pdf)

• Sousa, C., Batista, G., 2016. Constrained local and global consistency for semi-supervised learning. 2016 23rd International Conference on Pattern Recognition (ICPR), pp.1689-1694, IEEE. (https://projet.liris.cnrs.fr/imagine/pub/proceedings/ICPR-2016/media/files/1020.pdf)

• Qi, Y., Cinar, G., Souza, V., Batista, G., Wang, Y., Principe, J., 2015. Effective insect recognition using a stacked autoencoder with maximum correntropy criterion. 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-Jul, IEEE. (https://www.researchgate.net/profile/Vinicius-Alves-De-Souza/publication/280490285_Effective_Insect_Recognition_Using_a_Stacked_Autoencoder_with_Maximum_Correntropy_Criterion/links/55b62b9708ae9289a08aa6ef/Effective-Insect-Recognition-Using-a-Stacked-Autoencoder-with-Maximum-Correntropy-Criterion.pdf)

• Silva, D., de Souza, V., Batista, G., 2015. Music Shapelets for Fast Cover Song Recognition. ISMIR, pp.441-447. (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.727.8112&rep=rep1&type=pdf)

• de Sousa, C., Souza, V., Batista, G., 2015. An experimental analysis on time series transductive classification on graphs. 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-Aug, IEEE. (https://www.researchgate.net/profile/Celso-Sousa/publication/280306989_An_experimental_analysis_on_time_series_transductive_classification_on_graphs/links/55b0eb0808ae092e964fb734/An-experimental-analysis-on-time-series-transductive-classification-on-graphs.pdf)

• De Sousa, A., Batista, G., 2015. Robust multi-class graph transduction with higher order regularization. 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-Aug, IEEE. (https://www.researchgate.net/profile/Celso-Sousa/publication/280306897_Robust_Multi-class_Graph_Transduction_with_Higher_Order_Regularization/links/55b0eab708aec0e5f430dfac/Robust-Multi-class-Graph-Transduction-with-Higher-Order-Regularization.pdf)

• Souza, V., Batista, G., Souza-Filho, N., 2015. Automatic classification of drum sounds with indefinite pitch. 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-Aug, IEEE. (https://www.researchgate.net/profile/Vinicius-Alves-De-Souza/publication/280490270_Automatic_Classification_of_Drum_Sounds_with_Indefinite_Pitch/links/55b628f108aec0e5f436e2c6/Automatic-Classification-of-Drum-Sounds-with-Indefinite-Pitch.pdf)

• Parmezan, A., Batista, G., 2015. A study of the use of complexity measures in the similarity search process adopted by knn algorithm for time series prediction. 2015 IEEE 14th international conference on machine learning and applications (ICMLA), pp.45-51, IEEE. (https://www.researchgate.net/profile/Antonio-Parmezan/publication/300414605_A_Study_of_the_Use_of_Complexity_Measures_in_the_Similarity_Search_Process_Adopted_by_kNN_Algorithm_for_Time_Series_Prediction/links/5c3f0d44299bf12be3cb7ca6/A-Study-of-the-Use-of-Complexity-Measures-in-the-Similarity-Search-Process-Adopted-by-kNN-Algorithm-for-Time-Series-Prediction.pdf)

• Oliveira, L., Batista, G., 2015. Igmm-cd: a gaussian mixture classification algorithm for data streams with concept drifts. 2015 Brazilian Conference on Intelligent Systems (BRACIS), pp.55-61, IEEE. (https://www.researchgate.net/profile/Luan-Oliveira-10/publication/300409031_IGMM-CD_A_Gaussian_Mixture_Classification_Algorithm_for_Data_Streams_with_Concept_Drifts/links/5a333a38a6fdcc9b2d7774d5/IGMM-CD-A-Gaussian-Mixture-Classification-Algorithm-for-Data-Streams-with-Concept-Drifts.pdf)

• Souza, V., Silva, D., Batista, G., Gama, J., 2015. Classification of Evolving Data Streams with Infinitely Delayed Labels. IEEE International Conference on Machine Learning & Applications (ICMLA), pp.214-219. (http://repositorio.inesctec.pt/bitstreams/616888f4-611f-4105-9ab0-10b4e93270df/download)

• Souza, V., Silva, D., Gama, J., Batista, G., 2015. Data Stream Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency. SIAM International Conference on Data Mining (SDM), pp.873-881. (https://epubs.siam.org/doi/pdf/10.1137/1.9781611974010.98)

• Giusti, R., Silva, D., Batista, G., 2015. Time series classification with representation ensembles. International Symposium on Intelligent Data Analysis, pp.108-119, Springer, Cham. (https://link.springer.com/chapter/10.1007/978-3-319-24465-5_10)

• Lemes, C., Silva, D., Batista, G., 2014. Adding diversity to rank examples in anytime nearest neighbor classification. 2014 13th International Conference on Machine Learning and Applications, pp.129-134, IEEE. (https://www.researchgate.net/profile/Cristiano-Lemes/publication/273360729_Adding_Diversity_to_Rank_Examples_in_Anytime_Nearest_Neighbor_Classification/links/5760012b08aed884621dab79/Adding-Diversity-to-Rank-Examples-in-Anytime-Nearest-Neighbor-Classification.pdf)

• Souza, V., Silva, D., Batista, G., 2014. Extracting texture features for time series classification. 2014 22nd International Conference on Pattern Recognition, pp.1425-1430, IEEE. (https://www.researchgate.net/profile/Vinicius-Alves-De-Souza/publication/274733996_Extracting_Texture_Features_for_Time_Series_Classification/links/55b62e5e08aec0e5f436eb4b/Extracting-Texture-Features-for-Time-Series-Classification.pdf)

• De Sousa, C., Souza, V., Batista, G., 2014. Time series transductive classification on imbalanced data sets: an experimental study. 2014 22nd International Conference on Pattern Recognition, pp.3780-3785, IEEE. (https://www.researchgate.net/profile/Celso-Sousa/publication/274734074_Time_Series_Transductive_Classification_on_Imbalanced_Data_Sets_An_Experimental_Study/links/552d0ab50cf21acb092121f4/Time-Series-Transductive-Classification-on-Imbalanced-Data-Sets-An-Experimental-Study.pdf)

• Silva, D., Keogh, E., Batista, G., 2014. Automatic insect classification with Machine Learning techniques: a comparison of similarity and feature extraction approaches. Anais do XLI Seminário Integrado de Software e Hardware, pp.131-142, SBC. (https://sol.sbc.org.br/index.php/semish/article/download/10854/10722)

• Maletzke, A., Lee, H., Enrique, G., Batista, A., Coy, C., Fagundes, J., Chung, W., 2014. Time series classification with motifs and characteristics. Soft Computing for Business Intelligence, pp.125-138, Springer, Berlin, Heidelberg. (https://link.springer.com/chapter/10.1007/978-3-642-53737-0_8)

• Silva, D., Rossi, R., Rezende, S., Batista, G., 2014. Music Classification by Transductive Learning Using Bipartite Heterogeneous Networks. International Society of Music Information Retrieval Conference (ISMIR). (https://archives.ismir.net/ismir2014/paper/000263.pdf)

• Maletzke, A., Lee, H., Batista, G., Rezende, S., Machado, R., Voltolini, R., Maciel, J., Silva, F., 2013. Time series classification using motifs and characteristics extraction: a case study on ECG databases. Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support, pp.322-329, Atlantis Press. (https://www.atlantis-press.com/article/9654.pdf)

• Rakthanmanon, T., Keogh, E., 2013. Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping.. IJCAI, pp.3047-3051. (https://www.cpe.ku.ac.th/~fengtwr/paper/13IJCAI_Trillion_Short.pdf)

• Chen, Y., Hu, B., Keogh, E., Batista, G., 2013. DTW-D: time series semi-supervised learning from a single example. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.383-391. (https://www.researchgate.net/profile/Yanping-Chen-10/publication/262350999_DTW-D_time_series_semi-supervised_learning_from_a_single_example/links/54453a260cf2d62c304d79a6/DTW-D-time-series-semi-supervised-learning-from-a-single-example.pdf)

• de Sousa, C., Rezende, S., Batista, G., 2013. Influence of graph construction on semi-supervised learning. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.160-175, Springer, Berlin, Heidelberg. (https://link.springer.com/content/pdf/10.1007/978-3-642-40994-3_11.pdf)

• Silva, D., Papadopoulos, H., Batista, G., Ellis, D., 2013. A video compression-based approach to measure music structural similarity. International Society for Music Information Retrieval Conference, pp.95–10. (https://hal.archives-ouvertes.fr/hal-00924812/document)

• Giusti, R., Batista, G., 2013. An empirical comparison of dissimilarity measures for time series classification. 2013 Brazilian Conference on Intelligent Systems, pp.82-88, IEEE.. (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.1418&rep=rep1&type=pdf)

• Silva, D., De Souza, V., Batista, G., 2013. Time series classification using compression distance of recurrence plots. 2013 IEEE 13th International Conference on Data Mining, pp.687-696, IEEE. (https://www.academia.edu/download/54560656/icdm.2013.12820170928-2958-1g9dmup.pdf)

• Domingues, M., Cherman, E., Nogueira, B., Conrado, M., Rossi, R., de Padua, R., Marcacini, R., Souza, V., Batista, G., Rczendc, S., 2013. A comparative study of algorithms for recommending given names. 2013 Second International Conference on Informatics & Applications (ICIA), pp.66-71, IEEE. (https://www.researchgate.net/profile/Marcos-Domingues-4/publication/259006258_A_comparative_study_of_algorithms_for_recommending_given_names/links/5493048f0cf22d7925d5ec2f/A-comparative-study-of-algorithms-for-recommending-given-names.pdf)

• de Souza, V., Silva, D., Batista, G., 2013. Classification of data streams applied to insect recognition: Initial results. 2013 Brazilian conference on intelligent systems, pp.76-81, IEEE. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.3672&rep=rep1&type=pdf)

• Silva, D., De Souza, V., Batista, G., Keogh, E., Ellis, D., 2013. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. 2013 12th International conference on machine learning and applications, 1, pp.99-104, IEEE. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.401.2709&rep=rep1&type=pdf)

• Silva, D., Souza, V., Batista, G., Giusti, R., 2012. Spoken digit recognition in portuguese using line spectral frequencies. Ibero-American Conference on Artificial Intelligence, pp.241-250, Springer, Berlin, Heidelberg. (https://link.springer.com/chapter/10.1007/978-3-642-34654-5_25)

• Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E., 2012. Searching and mining trillions of time series subsequences under dynamic time warping. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.262-270. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6816304/)

• Alves, G., Silva, D., Prati, R., 2012. An experimental design to evaluate class imbalance treatment methods. 2012 11th International Conference on Machine Learning and Applications, 2, pp.95-101, IEEE. (https://www.researchgate.net/profile/Ronaldo-Prati/publication/234116198_An_Experimental_Design_to_Evaluate_Class_Imbalance_Treatment_Methods/links/0912f50f5472633965000000/An-Experimental-Design-to-Evaluate-Class-Imbalance-Treatment-Methods.pdf)

• Prati, R., Batista, G., 2012. A complexity-invariant measure based on fractal dimension for time series classification. International Journal of Natural Computing Research (IJNCR), 3(3), pp.59-73, IGI Global.

• Qiang, Z., Rakthanmanon, T., Batista, G., Keogh, E., 2012. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets. SIAM International Conference on Data Mining, pp.999-1010. (https://epubs.siam.org/doi/pdf/10.1137/1.9781611972825.86)

• Batista, G., Wang, X., Keogh, E., 2011. A Complexity-Invariant Distance Measure for Time Series. SDM-2011: Proceedings of SIAM International Conference on Data Mining. (https://epubs.siam.org/doi/pdf/10.1137/1.9781611972818.60)

• Batista, G., Keogh, E., Mafra-Neto, A., 2011. Counting and classifying mosquitoes from a distance with ultra cheap sensors. Annual Meeting of the American Mosquito Control Association. (https://nunojnunes.me/publications/GoodIT2021.pdf)

• Batista, G., Hao, Y., Keogh, E., Mafra-Neto, A., 2011. Towards automatic classification on flying insects using inexpensive sensors. 2011 10th International Conference on Machine Learning and Applications and Workshops, 1, pp.364-369, IEEE. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.2471&rep=rep1&type=pdf)

• Batista, G., Keogh, E., Neto, A., Rowton, E., 2011. SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.761-764, ACM. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/221653593_SIGKDD_demo_Sensors_and_software_to_allow_computational_entomology_an_emerging_application_of_data_mining/links/0046353220f158858f000000/SIGKDD-demo-Sensors-and-software-to-allow-computational-entomology-an-emerging-application-of-data-mining.pdf)

• Batista, G., Campana, B., Keogh, E., 2010. Classification of Live Moths Combining Texture, Color and Shape Primitives. Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on, pp.903-906, IEEE. (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.1480&rep=rep1&type=pdf)

• Giusti, R., Batista, G., 2010. Discovering Knowledge Rules with Multi-Objective Evolutionary Computing. Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on, pp.119-124, IEEE. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/221226345_Discovering_Knowledge_Rules_with_Multi-Objective_Evolutionary_Computing/links/5bb12a3c299bf13e605961cf/Discovering-Knowledge-Rules-with-Multi-Objective-Evolutionary-Computing.pdf)

• Batista, G., Prati, R., Monard, M., 2010. A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System. IFIP International Federation for Information Processing, 276(1). (https://link.springer.com/content/pdf/10.1007/978-0-387-09695-7_13.pdf)

• Batista, G., Silva, D., 2009. How k-Nearest Neighbor Parameters Affect its Performance. X Argentine Symposium on Artificial Intelligence. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.2787&rep=rep1&type=pdf)

• Prati, R., Batista, G., Monard, M., 2009. Data mining with imbalanced class distributions: concepts and methods.. IICAI, pp.359-376. (http://sites.labic.icmc.usp.br/pub/mcmonard/PratiIICAI09.pdf)

• Milaré, C., Batista, G., Carvalho, A., 2009. Avaliaç ao de uma Abordagem Hıbrida para Aprender com Classes Desbalanceadas: Resultados Experimentais com o Indutor CN2. IV Congresso da Academia Trinacional de Ciências, 2, pp.15. (http://sites.labic.icmc.usp.br/pub/gbatista/MilareC3N09.pdf)

• Giusti, R., Batista, G., Prati, R., 2008. Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules. Hybrid Intelligent Systems, 2008. HIS’08. Eighth International Conference on, pp.537-542, IEEE. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.5584&rep=rep1&type=pdf)

• Prati, R., Batista, G., Monard, M., 2008. A study with class imbalance and random sampling for a decision tree learning system. IFIP International Conference on Artificial Intelligence in Theory and Practice, pp.131-140, Springer, Boston, MA. (https://link.springer.com/content/pdf/10.1007/978-0-387-09695-7_13.pdf)

• Cestari, D., Maletzke, A., 2008. Avaliação do algoritmo de força-bruta para a identificação de padrões frequentes em séries temporais. Anais do III Congresso da Academia Trinacional de Ciências, Foz do Iguaçu, 1, pp.1. (https://www.academia.edu/download/44435405/Avaliacao_do_Algoritmo_de_Forca-Bruta_para_a_Identificacao_de_Padroes_Frequentes_em_Series_Temporais.pdf)

• Matsubara, E., Prati, R., Batista, G., Monard, M., 2008. Missing value imputation using a semi-supervised rank aggregation approach. Brazilian Symposium on Artificial Intelligence, pp.217-226, Springer, Berlin, Heidelberg. (ftp://nozdr.ru/biblio/kolxoz/Cs/CsLn/A/Advances%20in%20Artificial%20Intelligence%20-%20SBIA%202008,%2019%20conf.(LNCS5249,%20Springer,%202008)(ISBN%209783540881896)(303s)CsLn.pdf#page=231)

• Maletzke, A., Batista, G., Lee, H., Itaipu–PTI, P., 2008. Uma avaliação sobre a identificação de motifs em séries temporais. Anais do Congresso da Academia Trinacional de Ciências, 1, pp.1–10. (http://sites.labic.icmc.usp.br/pub/gbatista/MaletzkeC3N2008.pdf)

• Batista, G., Prati, R., Monard, M., Giusti, R., Milaré, C., 2007. Classificação associativa utilizando seleção e construção de regras: um estudo comparativo. Encontro Nacional de Inteligência Artificial (ENIA): Anais do Congresso da Sociedade Brasileira de Computação, pp.1321-1330. (https://repositorio.usp.br/item/001627500)

• Batista, G., Bazan, A., Monard, M., 2006. Balancing Training Data for Automated Annotation of Keywords: a Case Study Journal: Brazilian Workshop on Bioinformatics In WOB (2003). of Science in Computer Science. Department of Computer Science and Engineering. College of Engineering University of South Florida. Retrieved on 13th October, pp.35-43. (http://www.inf.ufrgs.br/maslab/pergamus/pubs/balancing-training-data-for.pdf)

• Matsubara, E., Monard, M., Batista, G., 2005. Utilizando algoritmos de aprendizado semi-supervisionados multi-visão como rotuladores de texto. Anais do Workshop em Tecnologia da Informação de da Linguagem Humana (TIL2005), pp.2108-2117. (http://www.nilc.icmc.usp.br/til/til2005/arq0107.pdf)

• Matsubara, E., Monard, M., Batista, G., 2005. Multi-view semi-supervised learning: An approach to obtain different views from text datasets. Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005, pp.97-104, IOS Press. https://books.google.com/books?hl=en&lr=&id=zS6rbp5B15AC&oi=fnd&pg=PA97&dq=Multi-view+semi-supervised+learning:+An+approach+to+obtain+different+views+from+text+datasets&ots=j6UpHDp8fo&sig=vNStSsnStHuGIxTUZpP9N8wZtvg)

• Batista, G., Prati, R., Monard, M., 2005. Balancing strategies and class overlapping. Advances in Intelligent Data Analysis VI, pp.741-741, Springer Berlin/Heidelberg. (https://www.academia.edu/download/54560666/ida2005.pdf)

• 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, Springer Berlin/Heidelberg. (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.9004&rep=rep1&type=pdf)

• Batista, G., Monard, M., Bazzan, A., 2004. Improving rule induction precision for automated annotation by balancing skewed data sets. International Symposium on Knowledge Exploration in Life Science Informatics, pp.20-32, Springer, Berlin, Heidelberg. (ftp://nozdr.ru/biblio/kolxoz/Cs/CsLn/K/Knowledge%20Exploration%20in%20Life%20Science%20Informatics,KELSI%202004(LNCS3303,%20Springer,%202004)(ISBN%203540239278)(259s).pdf#page=30)

• 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, Springer Berlin/Heidelberg. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.325&rep=rep1&type=pdf)

• Prati, R., Batista, G., Monard, M., 2004. Learning with class skews and small disjuncts. Brazilian Symposium on Artificial Intelligence, pp.296-306, Springer, Berlin, Heidelberg. (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.8165&rep=rep1&type=pdf)

• Batista, G., Monard, M., 2004. Sniffer: um Ambiente Computacional para Gerenciamento de Experimentos de Aprendizado de Máquina Supervisionado. Proceedings of the I WorkComp Sul. (https://www.researchgate.net/profile/Maria-Carolina-Monard/publication/255640879_SNIFFER_Um_Ambiente_Computacional_para_Gerenciamento_de_Experimentos_de_Aprendizado_de_M_aquina_Supervisionado/links/0f317536d3642106b8000000/SNIFFER-Um-Ambiente-Computacional-para-Gerenciamento-de-Experimentos-de-Aprendizado-de-M-aquina-Supervisionado.pdf)

• Prati, R., Batista, G., Monard, M., do Trabalhador Sao-Carlense, A., Postal, C., 2003. Uma experiência no balanceamento artificial de conjuntos de dados para aprendizado com classes desbalanceadas utilizando análise ROC. Proc. of the Workshop on Advances & Trends in AI for Problem Solving, 1, pp.28-33. (https://www.researchgate.net/profile/Ronaldo-Prati/publication/228784062_Uma_experiencia_no_balanceamento_artificial_de_conjuntos_de_dados_para_aprendizado_com_classes_desbalanceadas_utilizando_analise_ROC/links/00b7d53b40bd7ee273000000/Uma-experiencia-no-balanceamento-artificial-de-conjuntos-de-dados-para-aprendizado-com-classes-desbalanceadas-utilizando-analise-ROC.pdf)

• BATISTA, G., MONARD, M., 2003. Um estudo sobre a efetividade do método de imputação baseado no algoritmo k-vizinhos mais próximos. IV Workshop on Advances & Trends in AI Problem Solving, pp.1-Jun. (https://repositorio.usp.br/item/001349732)

• Monard, M., Batista, G., 2003. Graphical Methods for Classifier. Advances in Intelligent Systems and Robotics: LAPTEC 2003, 101, pp.59, John Wiley & Sons. (https://www.academia.edu/download/47025577/A_Survey_on_Graphical_Methods_for_Classi20160705-22587-dl91a3.pdf)

• Batista, G., Bazan, A., Monard, M., 2003. Balancing training data for automated annotation of keywords: a case study. Proceedings of the Second Brazilian Workshop on Bioinformatics, pp.35-43. (http://www.inf.ufrgs.br/maslab/pergamus/pubs/balancing-training-data-for.pdf)

• Monard, M., Batista, G., 2002. Learning with Skewed Class Distributions. Advances in Logic, Artificial Intelligence, and Robotics: LAPTEC 2002, 85, pp.173, IOS Press. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/311396259_Learning_with_skewed_class_distrihutions/links/5bbf4b4ea6fdccf29792bf41/Learning-with-skewed-class-distrihutions.pdf)

• Batista, G., Monard, M., 2002. A Study of K-Nearest Neighbour as an Imputation Method. HIS, 87(251-260), pp.48. (https://www.researchgate.net/profile/Gustavo-Batista-2/publication/220981745_A_Study_of_K-Nearest_Neighbour_as_an_Imputation_Method/links/09e41505b692bb7d26000000/A-Study-of-K-Nearest-Neighbour-as-an-Imputation-Method.pdf)

• Lorena, A., Batista, G., de Carvalho, A., Monard, M., 2002. The influence of noisy patterns on the performance of learning methods in the splice junction recognition problem. Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on, pp.31-36, IEEE. (https://www.academia.edu/download/54560662/The_Influence_of_Noisy_Patterns_in_the_P20170928-2952-11yv8uy.pdf)

• Lorena, A., Batista, G., De Carvalho, A., Monard, M., 2002. Splice junction recognition using machine learning techniques. Proceedings of the First Brazilian Workshop on Bioinformatics, pp.32-39. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.5432&rep=rep1&type=pdf)

• Batista, G., Monard, M., 2001. A study of K-nearest neighbour as a model-based method to treat missing data. Proceedings of the Argentine symposium on artificial intelligence, 30, pp.1-Sep. (https://www.academia.edu/download/54560654/A_Study_of_K-Nearest_Neighbour_as_a_Mode20170928-2952-q5jt6y.pdf)

• Batista, G., Carvalho, A., Monard, M., 2000. Applying one-sided selection to unbalanced datasets. MICAI 2000: Advances in Artificial Intelligence, pp.315-325, Springer Berlin/Heidelberg. (https://link.springer.com/chapter/10.1007/10720076_29)

• Baranauskas, J., Monarch, M., Batista, G., 2000. A computational environment for extracting rules from databases. WIT Transactions on Information and Communication Technologies, 25, WIT Press. (https://www.witpress.com/Secure/elibrary/papers/DATA00/DATA00031FU.pdf)

• Batista, G., CARVALHO, A., Monard, M., 1999. Aplicando Seleção Unilateral em Conjuntos de Exemplos Desbalanceados: Resultados Iniciais. XIX CONGRESSO NACIONAL DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO” EDUCAÇÃO E APRENDIZAGEM NA SOCIEDADE DA INFORMAÇÃO, 20, pp.327-340. (https://www.researchgate.net/profile/Maria-Carolina-Monard/publication/228967564_Aplicando_Selecao_Unilateral_em_Conjuntos_de_Exemplos_Desbalanceados_Resultados_Iniciais/links/0912f50bb975c9c0c7000000/Aplicando-Selecao-Unilateral-em-Conjuntos-de-Exemplos-Desbalanceados-Resultados-Iniciais.pdf)

• Monard, M., Milaré, C., Batista, G., 1998. A tool to explore explanation facilities in neural network. Proceedings of ACNN’98, pp.128-132. (https://repositorio.usp.br/item/001028059)

• Batista, G., Monard, M., 1997. A Computational Environment to Measure Machine Learning Systems Performance. Proceedings I ENIA, pp.41-45. (https://www.sciencedirect.com/science/article/pii/S0263224118300228)