Gustavo E. A. P. A. Batista

Publication Activity (10 Years)

Years Active: 2000-2022
Publications (10 Years): 33

Top Topics

Top Venues

Publications

Antonio Rafael Sabino Parmezan, Vinicius M. A. Souza, Arpita Seth, Indre Zliobaite, Gustavo E. A. P. A. Batista
Hierarchical classification of pollinating flying insects under changing environments. Ecol. Informatics 70 (2022)
Tiago Pinho da Silva, Antonio Rafael Sabino Parmezan, Gustavo E. A. P. A. Batista
Geographic Context-Based Stacking Learning for Election Prediction from Socio-economic Data. BRACIS (1) (2022)
Antonio Rafael Sabino Parmezan, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
Time Series Prediction via Similarity Search: Exploring Invariances, Distance Measures and Ensemble Functions. IEEE Access 10 (2022)
Tiago Pinho da Silva, Antonio Rafael Sabino Parmezan, Gustavo E. A. P. A. Batista
A Graph-Based Spatial Cross-Validation Approach for Assessing Models Learned with Selected Features to Understand Election Results. ICMLA (2021)
L. H. M. Jacintho, Tiago Pinho da Silva, Antonio Rafael Sabino Parmezan, Gustavo E. A. P. A. Batista
Analysing Spatio-Temporal Voting Patterns in Brazilian Elections Through a Simple Data Science Pipeline. J. Inf. Data Manag. 12 (1) (2021)
Lucas Tsutsui da Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
An Open-Source Tool for Classification Models in Resource-Constrained Hardware. CoRR (2021)
Jáder M. C. de Sá, Andre L. D. Rossi, Gustavo E. A. P. A. Batista, Luís Paulo F. Garcia
Algorithm Recommendation for Data Streams. ICPR (2020)
Antonio Rafael Sabino Parmezan, Diego Furtado Silva, Gustavo E. A. P. A. Batista
A Combination of Local Approaches for Hierarchical Music Genre Classification. ISMIR (2020)
Denis Moreira dos Reis, Marcílio de Souto, Elaine P. M. de Sousa, Gustavo E. A. P. A. Batista
Quantifying With Only Positive Training Data. CoRR (2020)
Waqar Hassan, André Gustavo Maletzke, Gustavo E. A. P. A. Batista
Accurately Quantifying a Billion Instances per Second. DSAA (2020)
Vinícius M. A. de Souza, Denis Moreira dos Reis, André Gustavo Maletzke, Gustavo E. A. P. A. Batista
Challenges in Benchmarking Stream Learning Algorithms with Real-world Data. CoRR (2020)
Vinícius M. A. de Souza, Denis Moreira dos Reis, André Gustavo Maletzke, Gustavo E. A. P. A. Batista
Challenges in benchmarking stream learning algorithms with real-world data. Data Min. Knowl. Discov. 34 (6) (2020)
André Gustavo Maletzke, Waqar Hassan, Denis Moreira dos Reis, Gustavo E. A. P. A. Batista
The Importance of the Test Set Size in Quantification Assessment. IJCAI (2020)
André Gustavo Maletzke, Denis Moreira dos Reis, Everton Alvares Cherman, Gustavo E. A. P. A. Batista
DyS: A Framework for Mixture Models in Quantification. AAAI (2019)
Antonio Rafael Sabino Parmezan, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
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. Inf. Sci. 484 (2019)
Diego Furtado Silva, Chin-Chia Michael Yeh, Yan Zhu, Gustavo E. A. P. A. Batista, Eamonn J. Keogh
Fast Similarity Matrix Profile for Music Analysis and Exploration. IEEE Trans. Multim. 21 (1) (2019)
Lucas Tsutsui da Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
EmbML Tool: Supporting the use of Supervised Learning Algorithms in Low-Cost Embedded Systems. ICTAI (2019)
André Gustavo Maletzke, Denis Moreira dos Reis, Gustavo E. A. P. A. Batista
Combining instance selection and self-training to improve data stream quantification. J. Braz. Comput. Soc. 24 (1) (2018)
Antonio Rafael Sabino Parmezan, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
Towards Hierarchical Classification of Data Streams. CIARP (2018)
Denis Moreira dos Reis, André Gustavo Maletzke, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Classifying and Counting with Recurrent Contexts. KDD (2018)
Diego Furtado Silva, Rafael Giusti, Eamonn J. Keogh, Gustavo E. A. P. A. Batista
Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Min. Knowl. Discov. 32 (4) (2018)
Diego Furtado Silva, Gustavo E. A. P. A. Batista
Elastic Time Series Motifs and Discords. ICMLA (2018)
Vinicius Souza, Tiago Pinho da Silva, Gustavo E. A. P. A. Batista
Evaluating Stream Classifiers with Delayed Labels Information. BRACIS (2018)
Denis Moreira dos Reis, André Gustavo Maletzke, Gustavo E. A. P. A. Batista
Unsupervised context switch for classification tasks on data streams with recurrent concepts. SAC (2018)
André Gustavo Maletzke, Denis Moreira dos Reis, Everton Alvares Cherman, Gustavo E. A. P. A. Batista
On the Need of Class Ratio Insensitive Drift Tests for Data Streams. LIDTA@ECML/PKDD (2018)
Vinícius Mourão Alves de Souza, Rafael Geraldeli Rossi, Gustavo E. A. P. A. Batista, Solange O. Rezende
Unsupervised active learning techniques for labeling training sets: An experimental evaluation on sequential data. Intell. Data Anal. 21 (5) (2017)
André Gustavo Maletzke, Denis Moreira dos Reis, Gustavo E. A. P. A. Batista
Quantification in Data Streams: Initial Results. BRACIS (2017)
Diego Furtado Silva, Chin-Chia Michael Yeh, Gustavo E. A. P. A. Batista, Eamonn J. Keogh
SiMPle: Assessing Music Similarity Using Subsequences Joins. ISMIR (2016)
Rafael Giusti, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Improved Time Series Classification with Representation Diversity and SVM. ICMLA (2016)
Diego Furtado Silva, Gustavo E. A. P. A. Batista
Speeding Up All-Pairwise Dynamic Time Warping Matrix Calculation. SDM (2016)
Denis Moreira dos Reis, Peter A. Flach, Stan Matwin, Gustavo E. A. P. A. Batista
Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test. KDD (2016)
Celso André R. de Sousa, Gustavo E. A. P. A. Batista
Constrained Local and Global Consistency for semi-supervised learning. ICPR (2016)
Diego Furtado Silva, Gustavo E. A. P. A. Batista, Eamonn J. Keogh
Prefix and Suffix Invariant Dynamic Time Warping. ICDM (2016)
Rafael Giusti, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Time Series Classification with Representation Ensembles. IDA (2015)
Gustavo E. A. P. A. Batista, Myriam Regattieri Delgado, Flávia Cristina Bernardini
ENIAC 2013 Special Issue. J. Intell. Robotic Syst. 80 (Supplement-1) (2015)
Vinícius M. A. de Souza, Diego Furtado Silva, João Gama, Gustavo E. A. P. A. Batista
Data Stream Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency. SDM (2015)
Diego Furtado Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
Music Shapelets for Fast Cover Song Recognition. ISMIR (2015)
Celso André R. de Sousa, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
An experimental analysis on time series transductive classification on graphs. IJCNN (2015)
Luan Soares Oliveira, Gustavo E. A. P. A. Batista
IGMM-CD: A Gaussian Mixture Classification Algorithm for Data Streams with Concept Drifts. BRACIS (2015)
Yu Qi, Goktug T. Cinar, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, Yueming Wang, José C. Príncipe
Effective insect recognition using a stacked autoencoder with maximum correntropy criterion. IJCNN (2015)
Diego Furtado Silva, Vinícius M. A. de Souza, Daniel P. W. Ellis, Eamonn J. Keogh, Gustavo E. A. P. A. Batista
Exploring Low Cost Laser Sensors to Identify Flying Insect Species - Evaluation of Machine Learning and Signal Processing Methods. J. Intell. Robotic Syst. 80 (Supplement-1) (2015)
Vinícius M. A. de Souza, Diego Furtado Silva, Gustavo E. A. P. A. Batista, João Gama
Classification of Evolving Data Streams with Infinitely Delayed Labels. ICMLA (2015)
Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, Nilson E. Souza-Filho
Automatic classification of drum sounds with indefinite pitch. IJCNN (2015)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Diego Furtado Silva
Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowl. Inf. Syst. 45 (1) (2015)
Antonio Rafael Sabino Parmezan, Gustavo E. A. P. A. Batista
A Study of the Use of Complexity Measures in the Similarity Search Process Adopted by kNN Algorithm for Time Series Prediction. ICMLA (2015)
Rosa Del Gaudio, Gustavo E. A. P. A. Batista, António Branco
Coping with highly imbalanced datasets: A case study with definition extraction in a multilingual setting. Nat. Lang. Eng. 20 (3) (2014)
Cristiano Inácio Lemes, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Adding Diversity to Rank Examples in Anytime Nearest Neighbor Classification. ICMLA (2014)
Celso André R. de Sousa, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study. ICPR (2014)
Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Oben Moses Tataw, Vinícius M. A. de Souza
CID: an efficient complexity-invariant distance for time series. Data Min. Knowl. Discov. 28 (3) (2014)
Yanping Chen, Adena Why, Gustavo E. A. P. A. Batista, Agenor Mafra-Neto, Eamonn J. Keogh
Flying Insect Classification with Inexpensive Sensors. CoRR (2014)
Vinícius M. A. de Souza, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Extracting Texture Features for Time Series Classification. ICPR (2014)
Diego Furtado Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Daniel P. W. Ellis
Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps. ICMLA (1) (2013)
Diego Furtado Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista
Time Series Classification Using Compression Distance of Recurrence Plots. ICDM (2013)
Celso André R. de Sousa, Solange O. Rezende, Gustavo E. A. P. A. Batista
Influence of Graph Construction on Semi-supervised Learning. ECML/PKDD (3) (2013)
Rafael Giusti, Gustavo E. A. P. A. Batista
An Empirical Comparison of Dissimilarity Measures for Time Series Classification. BRACIS (2013)
Yanping Chen, Bing Hu, Eamonn J. Keogh, Gustavo E. A. P. A. Batista
DTW-D: time series semi-supervised learning from a single example. KDD (2013)
Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn J. Keogh
Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. ACM Trans. Knowl. Discov. Data 7 (3) (2013)
Vinícius M. A. de Souza, Diego Furtado Silva, Gustavo E. A. P. A. Batista
Classification of Data Streams Applied to Insect Recognition: Initial Results. BRACIS (2013)
Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn J. Keogh
Searching and mining trillions of time series subsequences under dynamic time warping. KDD (2012)
Diego Furtado Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, Rafael Giusti
Spoken Digit Recognition in Portuguese Using Line Spectral Frequencies. IBERAMIA (2012)
Qiang Zhu, Gustavo E. A. P. A. Batista, Thanawin Rakthanmanon, Eamonn J. Keogh
A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets. SDM (2012)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista
A Complexity-Invariant Measure Based on Fractal Dimension for Time Series Classification. Int. J. Nat. Comput. Res. 3 (3) (2012)
Gustavo E. A. P. A. Batista, Xiaoyue Wang, Eamonn J. Keogh
A Complexity-Invariant Distance Measure for Time Series. SDM (2011)
Claudia Regina Milaré, Gustavo E. A. P. A. Batista, André C. P. L. F. de Carvalho
A hybrid approach to learn with imbalanced classes using evolutionary algorithms. Log. J. IGPL 19 (2) (2011)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
A Survey on Graphical Methods for Classification Predictive Performance Evaluation. IEEE Trans. Knowl. Data Eng. 23 (11) (2011)
Gustavo E. A. P. A. Batista, Yuan Hao, Eamonn J. Keogh, Agenor Mafra-Neto
Towards Automatic Classification on Flying Insects Using Inexpensive Sensors. ICMLA (1) (2011)
Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Agenor Mafra-Neto, Edgar Rowton
SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining. KDD (2011)
Rafael Giusti, Gustavo E. A. P. A. Batista
Discovering Knowledge Rules with Multi-Objective Evolutionary Computing. ICMLA (2010)
Claudia Regina Milaré, Gustavo E. A. P. A. Batista, André C. P. L. F. de Carvalho
A Study of the Influence of Rule Measures in Classifiers Induced by Evolutionary Algorithms. IEEE Intell. Informatics Bull. 11 (1) (2010)
Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh
Classification of Live Moths Combining Texture, Color and Shape Primitives. ICMLA (2010)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
Data mining with imbalanced class distributions: concepts and methods. IICAI (2009)
Edson Takashi Matsubara, Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
Missing Value Imputation Using a Semi-supervised Rank Aggregation Approach. SBIA (2008)
Rafael Giusti, Gustavo E. A. P. A. Batista, Ronaldo Cristiano Prati
Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules. HIS (2008)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System. IFIP AI (2008)
Gustavo E. A. P. A. Batista, Claudia Regina Milaré, Ronaldo Cristiano Prati, Maria Carolina Monard
A Comparison of Methods for Rule Subset Selection Applied to Associative Classification. Inteligencia Artif. 10 (32) (2006)
Edson Takashi Matsubara, Maria Carolina Monard, Gustavo E. A. P. A. Batista
Multi-view Semi-supervised Learning: An Approach to Obtain Different Views from Text Datasets. LAPTEC (2005)
Gustavo E. A. P. A. Batista, Ronaldo C. Prati, Maria Carolina Monard
Balancing Strategies and Class Overlapping. IDA (2005)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior. MICAI (2004)
Claudia Regina Milaré, Gustavo E. A. P. A. Batista, André Carlos Ponce de Leon Ferreira de Carvalho, Maria Carolina Monard
Applying Genetic and Symbolic Learning Algorithms to Extract Rules from Artificial Neural Networks. MICAI (2004)
Gustavo E. A. P. A. Batista, Maria Carolina Monard, Ana L. C. Bazzan
Improving Rule Induction Precision for Automated Annotation by Balancing Skewed Data Sets. KELSI (2004)
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
Learning with Class Skews and Small Disjuncts. SBIA (2004)
Gustavo E. A. P. A. Batista, Ronaldo C. Prati, Maria Carolina Monard
A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. 6 (1) (2004)
Gustavo E. A. P. A. Batista, Maria Carolina Monard
An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Appl. Artif. Intell. 17 (5-6) (2003)
Gustavo E. A. P. A. Batista, Ana L. C. Bazzan, Maria Carolina Monard
Balancing Training Data for Automated Annotation of Keywords: a Case Study. WOB (2003)
Ana Carolina Lorena, Gustavo E. A. P. A. Batista, André Carlos Ponce de Leon Ferreira de Carvalho, Maria Carolina Monard
Splice Junction Recognition using Machine Learning Techniques. WOB (2002)
Ana Carolina Lorena, Gustavo E. A. P. A. Batista, André Carlos Ponce de Leon Ferreira de Carvalho, Maria Carolina Monard
The Influence of Noisy Patterns in the Performance of Learning Methods in the Splice Junction Recognition Problem. SBRN (2002)
Gustavo E. A. P. A. Batista, Maria Carolina Monard
A Study of K-Nearest Neighbour as an Imputation Method. HIS (2002)
Gustavo E. A. P. A. Batista, André Carlos Ponce de Leon Ferreira de Carvalho, Maria Carolina Monard
Applying One-Sided Selection to Unbalanced Datasets. MICAI (2000)