Gustavo E. A. P. A. Batista

Artificial Intelligence · 2002–2019

A researcher in the Science corpus: 9 papers; trusts 24 researchers and is trusted by 69 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #3,534 of 35,345 by trust here, #5,252 by citations
• Signal Processing — 16 citations into it; #2,684 of 7,572 by trust here, #2,816 by citations
• Statistical Learning Theory — 11 citations into it
• Information Systems — 6 citations into it; #524 of 8,771 by trust here, #895 by citations
• Algorithms & Complexity — 5 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Gustavo E. A. P. A. Batista trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. J. Ross Quinlan
2. Subir Ghosh
3. Nitesh V. Chawla
4. Peter Clark
5. Gary M. Weiss
6. Cèsar Ferri
7. Arthur P. Dempster
8. Charles Peter Elkan
9. Jorma Laurikkala
10. Pedro M. Domingos

Who trusts Gustavo E. A. P. A. Batista overall

1. Alberto Fernández
2. Salvador Garcia
3. Francisco Herrera
4. Victoria López
5. Ronaldo Cristiano Prati
6. Nitesh V. Chawla
7. David A. Cieslak
8. Mikel Galar
9. Rohit J. Kate
10. Tuong Le

Papers from Gustavo E. A. P. A. Batista's perspective

Ordered by how strongly the researchers Gustavo E. A. P. A. Batista trusts endorse each paper. Same corpus, a different order for every researcher.

1. UCI Repository of machine learning databases
2. Et al
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. MetaCost: a general method for making classifiers cost-sensitive
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet classification with deep convolutional neural networks
8. The Foundations of Cost-Sensitive Learning
9. Long Short-Term Memory
10. Learning and making decisions when costs and probabilities are both unknown (Knowledge Discovery and Data Mining)

Their papers

• A Complexity-Invariant Distance Measure for Time Series (2011) — 345 citations
• An analysis of four missing data treatment methods for supervised learning (2003) — 921 citations
• 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 (2019) — 296 citations
• Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping (2012) — 1,125 citations
• Data mining with imbalanced class distributions: concepts and methods (2009) — 67 citations
• Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior (2004) — 356 citations
• A study of the behavior of several methods for balancing machine learning training data (2004) — 4,104 citations
• Balancing Training Data for Automated Annotation of Keywords: a Case Study (2003) — 377 citations
• A Study of K-Nearest Neighbour as an Imputation Method (2002) — 480 citations