articleTop 1% cited
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
Pattern Recognition · 2011 · Vol. 44(8) · pp. 1761–1776
Mikel Galar✉(Universidad Publica de Navarra)Alberto Fernández(Universidad de Jaén)Edurne Barrenechea(Universidad Publica de Navarra)Humberto Bustince(Universidad Publica de Navarra)Francisco Herrera(Universidad de Granada)
Imbalanced Data Classification TechniquesMachine Learning and Data ClassificationFace and Expression RecognitionComputer scienceArtificial intelligenceRandom subspace methodCascading classifiersMachine learningClassifier (UML)Binary numberEnsemble learningSupport vector machineClass (philosophy)
Funding
- Ministerio de Educación, Cultura y Deporte
Citations
762
FWCI
41.73
field-weighted impact
References
100
Percentile
100%
vs. same field & year
Citations per year
Cited by
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Pattern Recognition · 2016 · 1,163 citations
Deep learning with convolutional neural networks for EEG decoding and visualization
Human Brain Mapping · 2017 · 3,300 citations
References
Single-layer learning revisited: a stepwise procedure for building and training a neural network
Neurocomputing · 1990 · 823 citations
Handbook of Parametric and Nonparametric Statistical Procedures
Technometrics · 2004 · 4,697 citations
Classification by pairwise coupling
The Annals of Statistics · 1998 · 1,293 citations
Decision-making with a fuzzy preference relation
Fuzzy Sets and Systems · 1978 · 1,237 citations
Instance-Based Learning Algorithms
Machine Learning · 1991 · 4,094 citations
Statistical Learning Theory
Technometrics · 1999 · 26,915 citations
Index for rating diagnostic tests
Cancer · 1950 · 11,242 citations
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
Information Sciences · 2009 · 2,148 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
