A study of the behavior of several methods for balancing machine learning training data GE Batista, RC Prati, MC Monard ACM SIGKDD Explorations Newsletter 6 (1), 20-29, 2004 | 4501 | 2004 |
Searching and mining trillions of time series subsequences under dynamic time warping T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, ... Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 1301 | 2012 |
An analysis of four missing data treatment methods for supervised learning GE Batista, MC Monard Applied Artificial Intelligence 17 (5-6), 519-533, 2003 | 1122 | 2003 |
The ucr time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Queen, G Batista http://www.cs.ucr.edu/~eamonn/time_series_data/, 2015 | 1038 | 2015 |
A Study of K-Nearest Neighbour as an Imputation Method GE Batista, MC Monard HIS 87 (251-260), 48, 2002 | 533 | 2002 |
Balancing training data for automated annotation of keywords: a case study G Batista, AL Bazan, MC Monard Proceedings of the Second Brazilian Workshop on Bioinformatics, 35-43, 2003 | 484 | 2003 |
Class imbalances versus class overlapping: an analysis of a learning system behavior R Prati, G Batista, M Monard MICAI 2004: Advances in Artificial Intelligence, 312-321, 2004 | 478 | 2004 |
A Complexity-Invariant Distance Measure for Time Series G Batista, X Wang, E Keogh SDM-2011: Proceedings of SIAM International Conference on Data Mining, 2011 | 445 | 2011 |
CID: an efficient complexity-invariant distance for time series GE Batista, EJ Keogh, OM Tataw, VMA De Souza Data Mining and Knowledge Discovery 28 (3), 634-669, 2014 | 408 | 2014 |
Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, ... ACM Transactions on Knowledge Discovery from Data (TKDD) 7 (3), 1-31, 2013 | 320 | 2013 |
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 ARS Parmezan, VMA Souza, GE Batista Information sciences 484, 302-337, 2019 | 278 | 2019 |
The UCR time series classification archive HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... URL https://www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018 | 272 | 2018 |
Hexagon-ML: the UCR time series classification archive, October 2018 HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... | 227* | 2018 |
Class imbalance revisited: a new experimental setup to assess the performance of treatment methods RC Prati, GE Batista, DF Silva Knowledge and Information Systems 45, 247-270, 2015 | 212 | 2015 |
Fast unsupervised online drift detection using incremental kolmogorov-smirnov test DM dos Reis, P Flach, S Matwin, G Batista Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 179 | 2016 |
Flying insect classification with inexpensive sensors Y Chen, A Why, G Batista, A Mafra-Neto, E Keogh Journal of insect behavior 27 (5), 657-677, 2014 | 161 | 2014 |
DTW-D: time series semi-supervised learning from a single example Y Chen, B Hu, E Keogh, GE Batista Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 159 | 2013 |
Speeding up all-pairwise dynamic time warping matrix calculation DF Silva, GE Batista Proceedings of the 2016 SIAM International Conference on Data Mining, 837-845, 2016 | 157 | 2016 |
Pré-processamento de dados em aprendizado de máquinas supervisionado. GE BATISTA Tese (Doutorado)-Instituto de Ciências Matemáticas e de Computação …, 2003 | 156* | 2003 |
Curvas ROC para avaliação de classificadores RC Prati, G Batista, MC Monard Revista IEEE América Latina 6 (2), 215-222, 2008 | 155* | 2008 |