An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

By Mikel Galar, Alberto Fernández, Edurne Barrenechea Tartas, Humberto Bustince, Francisco Herrera. Pattern Recognition, 2011

779 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3fae7a4e843b8bcf21b86bfc848c9b23ece56d99

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Layered Ensemble Architecture for Time Series Forecasting (2016, background): “Following the suggestion from [58] and [59] and to facili-”
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014, background): “Nevertheless, the OVO strategy makes the size of the training samples even smaller, whereas the OVA strategy causes class imbalance in the training samples [13].”
Identification of grapevine varieties using leaf spectroscopy and partial least squares (2013, background): “One usual argument to motivate the use of the desired values {0,1} is that decomposing each problem into sub-problems yields easier to solve sub-problems (Galar et al., 2011), however, if the whole problem may be solved with good results there is no reason to decompose it.”
Multi-class Classification without Multi-class Labels (2019, background): “, 2000) which has had a long history in the literature, especially in ensemble methods and binarization techniques (Galar et al., 2011).”
PrivEdge: From Local to Distributed Private Training and Prediction (2020, positive): “An N -class classifier can be decomposed into N classifiers via binary [56] or one-class decomposition [57].”
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation (2015, background): “A general overview on ensemble methods is provided in [40].”
A survey of multiple classifier systems as hybrid systems (2014, background): “A comprehensive recent survey of binary classifier ensembles is [124].”
Measuring the Effectiveness of Various Features for Thematic Information Extraction From Very High Resolution Remote Sensing Imagery (2015, methodology): “one-versus-one scheme always outperforms the one-versus-all scheme in classification accuracies and robustness [25].”