Boosting support vector machines for imbalanced data sets

By Benjamin X. Wang, Nathalie Japkowicz. 2008

352 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Data preprocessing techniques for classification without discrimination (2011, background): “In Cost-Sensitive and Utility-Based learning [5,12,20,28,31], it is assumed that not all types of prediction errors are equal and not all examples are as important.”
Cost-sensitive learning methods for imbalanced data (2010, methodology): “Most of them focus on the manipulation at the data level (resampling methods) such as in [4], [5], [6], [7], [8], [9], [10] and the classifier level (changing the classifier internally) such as in [11], [12], [13], [14], [15], [16], [17], [18].”
Quantifying explainable discrimination and removing illegal discrimination in automated decision making (2013, background): “In Cost-Sensitive and Utility-Based learning [8,16,31,40,46], it is assumed that not all types of prediction errors are equal and not all examples are as important.”
Learning from Imbalanced Data (2009, methodology): “For instance, Jo and Japkowicz [27] used the random oversampling method discussed in Section 3.1.1, while our example in Fig.”
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data (2015, methodology): “Furthermore, they are mostly limited to the binary class problems [24, 28], do not perform joint feature and classifier learning and do not explore computer vision tasks which inherently have imbalanced class distributions.”
SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory (2012, background): “In [41] a system is presented that combines these two general solutions (data and algorithm level) obtaining good results.”
Under Consideration for Publication in Knowledge and Information Systems Improving Svm Classification on Imbalanced Time Series Data Sets with Ghost Points (neutral): “For example, [38] discusses two major approaches to converting traditional classifiers into cost-sensitive classifiers, and [28] combines modifying the classifier with changing the data distribution.”
Calibrated asymmetric surrogate losses (2012, background): “Examples include boosting algorithms [5, 6, 7, 9, 13, 16, 20, 29, 33, 34, 36, 38, 39, 40], support vector machines [1, 4, 8, 10, 12, 18, 22, 37, 41, 42], and the perceptron [11].”