Publications by Gustavo E. A. P. A. Batista

Book Chapters

Maletzke, A. G., Lee, H. D., Batista, G. E. A. P. A., Coy, C. S. R., Fagundes, J. J., Chung, W. F. Time Series Classification with Motifs and Characteristics. Studies in Computational Intelligence. 1ed.: Springer Berlin Heidelberg, 2014, v. , p. 125-138.

Journal Publications 2015

Silva, D. F., Souza, V. M. A., Ellis, D. P. W., Keogh, E. J., Batista, G. E. A. P. A. Exploring Low Cost Laser Sensors to Identify Flying Insect Species. Journal of Intelligent & Robotic Systems, 2015.

Journal Publications 2014

Prati, R. C., Batista, G. E. A. P. A., Silva, D. F. Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowledge and Information Systems, 2014.
Paper website.

Chen, Y., Why, A., Batista, G. E. A. P. A., Mafra-Neto, A., Keogh, E. Flying Insect Classification with Inexpensive Sensors. Journal of Insect Behavior, 2014.
Paper website.

Chen, Y., Why, A., Batista, G. E. A. P. A., Mafra-Neto, A., Keogh, E. Flying Insect Detection and Classification with Inexpensive Sensors. Journal of Visualized Experiments, 2014.

Batista, G. E. A. P. A., Keogh, E., Tataw, O. M., Souza, V. M. A. CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 2014.
Paper website.

Gaudio, R., Batista, G. E. A. P. A., Branco, A. Coping with highly imbalanced datasets: A case study with definition extraction in a multilingual setting. Natural Language Engineering, 2014.

Journal Publications 2013

Rakthanmanon, T., Campana, B., Mueen, A., Batista, G. E. A. P. A., Westover, B., Qiang Z., Zakaria, J., Keogh, E. Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences under Dynamic Time Warping. ACM Ttransactions on Knowledge Discovery from Data, 7(3): 10, 2013.
Paper website.

Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A. A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English. Acta Scientiarum (UEM), 35(4), p. 621-628, 2013.

Journal Publications 2012 and Earlier

Prati, R. C., Batista, G. E. A. P. A. A Complexity-Invariant Measure Based on Fractal Dimension for Time Series Classification. International Journal of Natural Computing Research, v. 3, p. 59-73, 2012.

Milaré, C. R., Batista, G. E. A. P. A., Carvalho, A. C. P. L. F. A hybrid approach to learn with imbalanced classes using evolutionary algorithms. Logic Journal of the IGPL, v. 19, p. 293-303, 2011.

Prati, R. C., Batista, G. E. A. P. A., Monard, M. C. A Survey on Graphical Methods for Classification Predictive Performance Evaluation. IEEE Transactions on Knowledge and Data Engineering, v. 23, p. 1601-1618, 2011.

Milaré, C. R., Batista, G. E. A. P. A., Carvalho, A. C. P. L. F. A Study of the Influence of Rule Measures in Classifiers Induced by Evolutionary Algorithms. IEEE Intelligent Informatics Bulletin, v. 11, p. 8-13, 2010.

Prati, R. C., Batista, G. E. A. P. A., Monard, M. C. Curvas ROC para a Avaliação de Classificadores. Revista IEEE América Latina, 2008.

Prati, R. C., Batista, G. E. A. P. A., Monard, M. C. A Hybrid Wrapper/Filter Approach for Feature Subset Selection. Electronic Journal of Sadio, v.8, p. 12-24, 2008. A previous version of this paper was published in ASAI 2007.

Batista, G. E. A. P. A., Milaré, C. R., Prati, R. C., Monard, M. C. A Comparison of Methods for Rule Subset Selection Applied to Associative Classification. Inteligencia Artificial, v. 10, n. 32, p. 29-35, 2006.

Batista, G. E. A. P. A., Prati, R. C., Monard, M. C. A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explorations, v. 6, n. 1, p. 20-29, 2004. Special issue on Learning from Imbalanced Datasets, Guest edited by Nitesh Chawla, Nathalie Japkowicz, Aleksander Kolcz.

Batista, G. E. A. P. A., Monard, M. C. An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence, v. 17, n. 5-6, p. 519-533, 2003.

Batista, G. E. A. P. A., Bazzan, A. L. C., Monard, M. C. Balancing Training Data for Automated Annotation of Keywords: a Case Study. Revista Tecnologia da Informação, v. 3, n. 2, p. 15-20, 2003.

Conference Publications 2016

Dos Reis, D. M., Flach, P., Matwin, S., Batista, G. E. A. P. A. Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test. In: ACM SIGKDD International Conference (ACM-KDD), 2016. p. 1545.

Silva, D. F., Batista, G. E. A. P. A. Speeding Up All-Pairwise Dynamic Time Warping Matrix Calculation. In: SIAM International Conference on Data Mining (SIAM-SDM), 2016.

Silva, D. F., Batista, G. E. A. P. A., Keogh, E. J. On the Effect of Endpoints on Dynamic Time Warping. In: 2nd SIGKDD Workshop On Mining and Learning From Time Series, 2016.

Silva, D. F., Yeh, C. M., Batista, G. E. A. P. A., Keogh, E. J. SiMPle: Assessing Music Similarity Using Subsequences Joins. In: 16th International Society for Music Information Retrieval Conference (ISMIR), 2016, p. 23-29.

Sousa, C. A. R., Batista, G. E. A. P. A. Constrained Local and Global Consistency for Semi-supervised Learning. In: 23rd International Conference on Pattern Recognition (ICPR), 2016.

Silva, D. F., Batista, G. E. A. P. A., Keogh, E. J. Prefix and Suffix Invariant Dynamic Time Warping. In: IEEE International Conference on Data Mining (IEEE-ICDM), 2016.

Giusti, R., Silva, D. F., Batista, G. E. A. P. A. Improved Time Series Classification with Representation Diversity and SVM. In: IEEE International Conference on Machine Learning and Applications (IEEE-ICMLA), 2016.

Conference Publications 2015

Souza, V. M. A., Silva, D. F., Gama, J., Batista, G. E. A. P. A. Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency. In: SIAM International Conference on Data Mining (SDM), 2015.

Souza, V. M. A., BATISTA, G. E. A. P. A., Souza-Filho, N. E. Automatic Classification of Drum Sounds with Indefinite Pitch. In: International Joint Conference on Neural Networks (IJCNN), 2015.

Sousa, C. A. R., Souza, V. M. A., BATISTA, G. E. A. P. A. An Experimental Analysis on Time Series Transductive Classification on Graphs. In: International Joint Conference on Neural Networks (IJCNN), 2015.

Qi, Y., Cinar, G., Souza, V. M. A., BATISTA, G. E. A. P. A., Wang, Y., Principe, J. Effective Insect Recognition Using a Stacked Autoencoder with Maximum Correntropy Criterion. In: International Joint Conference on Neural Networks (IJCNN), 2015.

Sousa, C. A. R., Batista, G. E. A. P. A. Robust multi-class graph transduction with higher order regularization. In: International Joint Conference on Neural Networks (IJCNN), 2015.

Conference Publications 2014

Lemes, C. I., Silva, D. F., Rossi, R. G., Rezende, S. O., Batista, G. E. A. P. A. Adding Diversity to Rank Examples in Anytime Nearest Neighbor Classification. In: IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'14), 2014.

Silva, D. F., Rossi, R. G., Rezende, S. O., Batista, G. E. A. P. A. Music Classification by Transductive Learning Using Bipartite Heterogeneous Networks. In: The International Society for Music Information Retrieval Conference (ISMIR'14), 2014.

Souza, V. M. A., Silva, D. F., Batista, G. E. A. P. A. Extracting Texture Features for Time Series Classification. In: International Conference on Pattern Recognition (ICPR'14), 2014.

Sousa, C. A. R., Souza, V. M. A., Batista, G. E. A. P. A. Time series transductive classification on imbalanced data sets: an experimental study. In: International Conference on Pattern Recognition (ICPR'14), 2014.

Conference Publications 2013

Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A. Time Series Classification Using Compression Distance of Recurrence Plots. In: IEEE International Conference on Data Mining (ICDM'13), 2013.

Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., Keogh, E., Ellis, D. P. W. Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps. In: IEEE International Conference on Machine Learning and Applications (ICMLA'13), 2013.

Silva, D. F., Papadopoulos, H., Batista, G. E. A. P. A., Ellis, D. P. W. A Video Compression-based Approach To Measure Music Structural Similarity. In: International Society for Music Information Retrieval Conference (SMIR'13), 2013.

Giusti, R., Batista, G. E. A. P. A. An Empirical Comparison of Dissimilarity Measures for Time Series Classification. In: Brazilian Conference on Intelligent Systems (BRACIS'13), 2013.

Souza, V. M. A., Silva, D. F., Batista, G. E. A. P. A. Classification of Data Streams Applied to Insect Recognition: Initial Results. In: Brazilian Conference on Intelligent Systems (BRACIS, 13), 2013.

Souza, V. M. A., Silva, D. F., Garcia, P. R. P., Batista, G. E. A. P. A. Avaliação de classificadores para o reconhecimento automático de insetos. In: Encontro Nacional de Inteligência Artificial e Computacional (ENIAC'13), 2013.

Sousa, C. A. R., Rezende, S. O., Batista, G. E. A. P. A. Influence of Graph Construction on Semi-supervised Learning. In: European Conference on Machine Learning (ECML'13), 2013.
Paper website

Rakthanmanon, T, Campana, B., Mueen, A., Batista, G. E. A. P. A., Westover, B., Zhu, Q., Zakaria, J., Keogh, E. Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping. In: International Joint Conference on Artificial Intelligence (IJCAI'13), Pequim, China, 2013.

Chen, Y., Hu, B., Keogh, E., Batista, G. E. A. P. A. DTW-D: Time Series Semi-Supervised Learning from a Single Example. In: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'13), 2013, Chicago.
Paper website

Conference Publications 2012

Batista, G. E. A. P. A., Silva, D. F., Prati, R. C. An Experimental Design to Evaluate Class Imbalance Treatment Methods. In: Eleventh International Conference on Machine Learning and Applications (ICMLA'12), 2012, p. 95-101.

Prati, R. C., Batista, G. E. A. P. A. Distância invariante à complexidade baseada em dimensão fractal para classificação de séries temporais. In: Encontro Nacional de Inteligência Artificial (ENIA'12), 2012.

Giusti, R., Batista, G. E. A. P. A. Descoberta de regras de conhecimento utilizando computação evolutiva multi-objetivo. In: Concurso de Tese e Dissertações em Inteligência Artificial (CTDIA'12), 2012.

Rakthanmanon, T., Campana, B., Mueen, A., Batista, G. E. A. P. A., Westover, B., Zhu, Q., Zakaria, J., Keogh, E. Searching and mining trillions of time series subsequences under dynamic time warping. In: 18th ACM SIGKDD international conference (KDD'12), 2012, p. 262-270, best research paper award.
Paper website.

Silva, D. F., Souza, V. M. A., Batista, Gustavo E. A. P. A., Giusti, R. Spoken Digit Recognition in Portuguese Using Line Spectral Frequencies. In: 13th Ibero-American Conference on Artificial Intelligence (IBERAMIA'12), 2012, Lecture Notes on Artificial Intelligence, v. 7637. p. 241-250.
Paper website

Qiang Zhu, Batista, Gustavo E. A. P. A., Thanawin Rakthanmanon, Keogh, E. J. A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets. In: SIAM International Conference on Data Mining (SDM'12), 2012, p. 999-1010.
Paper website

Conference Publications 2011

Batista, G. E. A. P. A, Wang, X., Keogh, E. J. A Complexity-Invariant Distance Measure for Time Series. In: SIAM Conference on Data Mining (SDM'11), p. 699-710, 2011, Mesa, Arizona.
Paper website

Batista, G. E. A. P. A, Keogh, E. J., Mafra Neto, A. Sensors and software to allow computational entomology, an emerging application of data mining. In: 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11), p. 761-764, 2011, San Diego, CA.

Batista, G. E. A. P. A, Keogh, E. J., Mafra Neto, A. Towards Automatic Classification on Flying Insects using Inexpensive Sensors. In: 10th International Conference on Machine Learning and Applications (ICMLA'11), 2011.

Batista, G. E. A. P. A., Keogh, E. J., Mafra Neto, A. Counting and classifying mosquitoes from a distance with ultra cheap sensors. In: Annual Meeting of the AMCA, 2011.

Batista, G. E. A. P. A, Keogh, E. J., Mafra Neto, A. Towards Automatic Classification on Flying Insects using Inexpensive Sensors. In: 10th International Conference on Machine Learning and Applications (ICMLA'11), 2011.

Conference Publications 2010

Batista, G. E. A. P. A, Campana, B. J. L., Keogh, E. J. Classification of Live Moths Combining Texture, Color and Shape Primitives. In: 9th International Conference on Machine Learning and Applications (ICMLA'10), p. 903-906, 2010.

Giusti, R., Batista, G. E. A. P. A. Discovering Knowledge Rules with Multi-Objective Evolutionary Computing. In: 9th International Conference on Machine Learning and Applications (ICMLA'10), p. 119-124, 2010.

Conference Publications 2009

Batista, G. E. A. P. A, Silva, D. F. How k-Nearest Neighbor Parameters Affect its Performance. In: Argentine Symposium on Artificial Intelligence (ASAI'09), 2009.
Paper website

Milaré, C. R., Batista, G. E. A. P. A., Carvalho, A. C. P. L. F. A Hybrid Approach to Learn with Imbalanced Classes using Evolutionary Algorithms. In: International Conference Computational and Mathematical Methods in Science and Engineering, 2009.

Silva, D. F., Batista, G. E. A. P. A. Uma Comparação Experimental de Métodos de Imputação de Valores Desconhecidos. In: Congresso da Academia Trinacional de Ciências, 2009.

Milaré, C. R., Batista, G. E. A. P. A., Carvalho, A. C. P. L. F. Avaliação de uma Abordagem Híbrida para Aprender com Classes Desbalanceadas: Resultados Experimentais com o Indutor CN2. In: Congresso da Academia Trinacional de Ciências, 2009.

Maletzke, A. G., Batista, G. E. A. P. A., Lee, H. D., Wu, F. C. Mineração de Séries Temporais por meio da Extração de Características e da Identificação de Motifs. In: Encontro Nacional de Inteligência Artificial(ENIA'09), 2009.

Prati, R. C., Batista, G. E. A. P. A., Monard, M. C. Data mining with imbalanced class distributions: concepts and methods. In: 4th Indian International Conference on Artificial Intelligence, 2009.

Ferrero, C. A., Maletzke, A. G., Lee, H. D., Batista, G. E. A. P. A., Lee, H. D. Extração de Padrões e Construção de Modelos Simbólicos para Previsão de Dados Temporais. In: Congresso da Academia Trinacional de Ciências, 2009.

Conference Publications 2008

Giusti, R., Batista, G. E. A. P. A., Prati, R. C. Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules. In: 8th International Conference on Hybrid Intelligent Systems (HIS'08), 2008.

Prati, R. C., Batista, G. E. A. P. A., Monard, M. C. A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System. In: IFIP World Computer Congress - TC 12 IFIP International Conference on Artificial Intelligence in Theory and Practice (IFIP AI), 2008.

Matsubara, E. T., Prati, R. C., Batista, G. E. A. P. A., Monard, M. C. Missing Value Imputation using a Semi-supervised Rank Aggregation Approach. In: Simpósio Brasileiro em Inteligência Artificial, 2008.

Maletzke, A. G., Batista, G. E. A. P. A., Lee, H. D. Uma Avaliação sobre a Identificação de Motifs em Séries Temporais. In: Congresso da Academia Trinacional de Ciências, 2008.

Cestari, D. M., Maletzke, A. G., Batista, G. E. A. P. A. Avaliação do Algoritm Força-Bruta para a Identificação de Padrões Frequentes em Séries Temporais. In: Congresso da Academia Trinacional de Ciências, 2008.

Rosada, L. F., Batista, G. E. A. P. A. A Biblioteca DOL para Mineração de Dados e Séries Temporais. In: Congresso da Academia Trinacional de Ciências, 2008.

Conference Publications 2007

Prati, R. C., Batista, G. E. A. P. A., Monard, M. C.A Hybrid Wrapper/FilterApproach for Feature Subset Selection. In: Argentine Symposium on Artificial Intelligence, 2007.

Batista, G. E. A. P. A., Prati, R. C., Monard, M. C., Giusti, R., Milaré, C. R. Classificação Associativa utilizando Seleção e Construção de Regras: um Estudo Comparativo. In: Encontro Nacional de Inteligência Artificial, 2007, v. 1, p. 1321-1330.

Conference Publications 2006

Batista, G. E. A. P. A. , C. R. Milaré, R. C. Prati, M. C. Monard. A Comparison of Methods for Rule Subset Selection Applied to Associative Classification. In: Proceedings of the VIII Argentine Symposium on Artificial Intelligence, 2006, p. 45-54.

Conference Publications 2005

Batista, G. E. A. P. A., R. C. Prati, M. C. Monard. 2005. Balancing Strategies and Class Overlapping. In: Proceeding of the VI International Symposium on Intelligent Data Analysis, 2005, Springer-Verlag, LNAI 3646, p. 24-35.

Matsubara, E. T., M. C. Monard, and G. E. A. P. A. Batista. 2005. Utilizando Algoritmos de Aprendizado Semi-supervisionados Multi-visão como Rotuladores de Textos. in: Workshop em Tecnologia da Informação e da Linguagem Humana, 2005, p. 2108-2117.

Matsubara, E.T., M. C. Monard, and G. E. A. P. A. Batista. 2005. Multi-view Semi-supervised Learning: An Approach to Obtain Different Views from Text Datasets. In: Fifth Congress Of Logic Applied to Technology, Himeji, Japão, 2005, IOS Press, p. 97-104.

Conference Publications 2004

Matsubara, E.T., M. C. Monard, and G. E. A. P. A. Batista. 2004. Aprendizado Semi-supervisionado Multi-visão para a Classificação de Bases de Texto. In: V Workshop on Artificial Intelligence, Arica, Chile, 2004.

Batista, G. E. A. P. A., M. C. Monard, and A. L. C. Bazzan. 2004. Improving Rule Induction Precision for Automated Annotation by Balancing Skewed Data Sets. In: I Knowledge Exploration in Life Science Informatics, 2004, Springer-Verlag, LNAI 3303, p. 20-32.

Prati, R. C., G. E. A. P. A. Batista, and M. C. Monard. 2004. Learning with Class Skews and Small Disjuncts. In: XVII Brazilian Symposium on Artificial Intelligence, 2004, Springer-Verlag, LNAI, 3171, p. 296-306.

Batista, G. E. A. P. A., and M. C. Monard. 2004. Sniffer: um Ambiente Computacional para Gerenciamento de Experimentos de Aprendizado de Máquina Supervisionado. In: I WorkComp Sul, 2004, p.13-24.

Prati, R. C., G. E. A. P. A. Batista, and M. C. Monard. 2004. Class Imbalances Versus Class Overlapping: an Analysis of a Learning System Behavior. In: Mexican International Conference on Artificial Intelligence, 2004, Springer-Verlag, LNAI 2972, p. 312-321.

Milaré, C. R., G. E. A. P. A. Batista, A. C. P. L. F. Carvalho, and M. C. Monard. 2004. Applying Genetic and Symbolic Learning Algorithms to Extract Rules from Artificial Neural Networks. In: Mexican International Conference on Artificial Intelligence, 2004, Springer-Verlag, LNAI 2972, p. 833-843.

Conference Publications 2003

Batista, G. E. A. P. A., A. L. C. Bazzan, and M. C. Monard. 2003. Balancing Training Data for Automated Annotation of Keywords: a Case Study. In: Segundo Workshop Brasileiro em Bioinformática, 2003.

Batista, G. E. A. P. A., and M. C. Monard. 2003. Um Estudo Sobre a Efetividade do Método de Imputação Baseado no Algoritmo k-Vizinhos mais Próximos. In: IV Workshop on Advances & Trends in AI for Problem Solving, 2003.

Prati, R. C., G. E. A. P. A. Batista, and M. C. Monard. 2003. Uma Experiência no Balanceamento Artificial de Conjuntos de Dados para Aprendizado com Classes Desbalanceadas utilizando Análise ROC. In: IV Workshop on Advances & Trends in AI for Problem Solving , 2003.

Monard, M. C., and G. E. A. P. A. Batista. 2003. Graphical Methods for Classifier Performance Evaluation. In: Fourth Congress of Logic Applied to Technology, Advances in Logic, Artificial Intelligence and Robotics. IOS Press, 2003. p. 59-67.

Conference Publications 2002

Batista, G. E. A. P. A., and M. C. Monard. 2002. A Study of K-Nearest Neighbour as an Imputation Method. In: Second International Conference on Hybrid Intelligent Systems, 2002, IOS Press, v. 87, p. 251-260.

Batista, G. E. A. P. A., and M. C. Monard. 2002. An Analysis of Four Missing Data Treatment Methods for Supervised Learning. In: First International Workshop on Data Cleaning and Preprocessing, 2002, p. 142-152.

Monard, M. C., and G. E. A. P. A. Batista. 2002. Learning with Skewed Class Distributions. In: Terceiro Congresso de Lógica Aplicada à Tecnologia, IOS Press, 2002, p. 173-180.

Lorena, A. C., G. E. A. P. A. Batista, A. C. P. L. F. Carvalho, and M. C. Monard. 2002. Splice Junction Recognition Using Machine Learning Techniques. In Anais do Primeiro Workshop Brasileiro de Bioinformática, 2002, p. 32-39.

Lorena, A. C., G. E. A. P. A. Batista, A. C. P. L. F. Carvalho, and M. C. Monard. 2002. The Influence of Noisy Patterns in the Performance of Learning Methods in the Splice Junction Recognition Problem. In: VII Brazilian Simposium on Neural Networks,IEEE Computer Press, 2002

Conference Publications 2001 and earlier

Batista, G. E. A. P. A., and M. C. Monard. 2001. A Study of K-Nearest Neighbour as a Model-Based Method to Treat Missing Data. In Proceedings of the Argentine Symposium on Artificial Intelligence, Buenos Aires, 2001, v. 30, p. 1-9.

Batista, G. E. A. P. A., and M. C. Monard. 2001. Uma Proposta para o Tratamento de Valores Desconhecidos Utilizando o Algoritmo K-Vizinhos mais Próximos. In Anais do V Simpósio Brasileiro de Automação Inteligente, Canela, RS, 2001.

Baranauskas, J. A., M. C. Monard, and G. E. A. P. A. Batista. 2000. A Computational Environment for Extracting Rules from Databases. In Proceedings of the Second International Conference on Data Mining, Cambridge, 2000, p. 321-330.

Batista, G. E. A. P. A., A. C. P. L. F. Carvalho, and M. C. Monard. 2000. Applying One-sided Selection to Unbalanced Datasets. In Proceedings of the Mexican International Conference on Artificial Intelligence, Acapulco, 2000, p. 315-325.

Batista, G. E. A. P. A., and M. C. Monard. 1999. Aplicando Seleção Unilateral em Conjuntos de Exemplo Desbalanceados: Resultados Iniciais. In Anais do II Encontro Nacional de Inteligência Artificial, Rio de Janeiro, 1999, p. 327-340.

Monard, M. C., C. R. Milaré, and G. E. A. P. A. Batista. 1998. A Tool to Explore Explanation Facilities in Neural Networks. In Proceedings of Australian Conference on Neural Networks, Australian Conference on Neural Networks, 1998, p. 128-132.

Batista, G. E. A. P. A., and M. C. Monard. 1997. AMPSAM: Um Ambiente Computacional para Medir a Performance de Sistemas de Aprendizado de Máquina. In Anais do I Encontro Nacional de Inteligência Artificial, Brasília, 1997, p. 41-45.

Milaré, C. R., G. E. A. P. A. Batista, and M. C. Monard. 1997. Uma Ferramenta para Extração de Conhecimento de Redes Neurais. In Anais do XXV Seminário Integrado de Software e Hardware, Brasília, 1997, p. 59-70.

Technical Reports

Batista, G. E. A. P. A., and M. C. Monard. 2003. Descrição da Arquitetura e do Projeto do Ambiente Computacional Discover Learning Environment - DLE. São Carlos: Relatório Técnico Nr. 187, ICMC-USP.

Batista, G. E. A. P. A., and M. C. Monard. 2003. Experimental Comparison of K-Nearest Neighbour and Mean or Mode Imputation Methods with the Internal Strategies Used by C4.5 an CN2 to Treat Missing Data. São Carlos: Relatório Técnico Nr. 186, ICMC-USP.

Gomes, A. K., F. C. Bernardini, M. C. Monard, and G. E. A. P. A. Batista. 2002. Uma Sintaxe Padrão Prolog para Classificadores Simbólicos. São Carlos: Relatório Técnico, Nr. 154, ICMC-USP.

Kemp, A. H., G. E. A. P. A. Batista, and M. C. Monard. 2001. Descrição da Implementação dos Métodos Estatísticos de Resampling do Ambiente Discover. São Carlos: Relatório Técnico, Nr. 143, ICMC-USP.

Batista, G. E. A. P. A., and M. C. Monard. 1998. Descrição da Implementação dos Métodos Estatísticos de Resampling do Ambiente AMPSAM. São Carlos: Relatório Técnico, Nr. 68, ICMC-USP.

Batista, G. E. A. P. A., and M. C. Monard. 1997. Descrição da Implementação PROLOG de uma Ferramenta para Extração de Conhecimento de Redes Neurais. São Carlos: Relatório Técnico, Nr. 54, ICMC-USP.

PhD Thesis and MSc Dissertation

PhD. Thesis. Batista, G. E. A. P. A. Pré-processamento de Dados em Aprendizado de Máquina Supervisionado (Data Pre-processing in Supervised Machine Learning). Department of Computer Science and Statistics. Universidade de São Paulo, 2003.
Available in USP Digital Library.

MSc. Dissertation. Batista, G. E. A. P. A. Um Ambiente de Avaliação de Algoritmos de Aprendizado de Máquina Utilizando Exemplos (An Environment to Evaluate the Performance of Machine Learning Algorithms). Department of Computer Science and Statistics. University of São Paulo, 1997.
Available in USP Digital Library.