Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

By Hongyu Guo, Herna Lydia Viktor. 2004

659 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

AutoML: A Survey of the State-of-the-Art (2019, methodology): “[69] propose to combines boosting method with data generation to enhance the”
A systematic study of the class imbalance problem in convolutional neural networks (2017, methodology): “DataBoost-IM, on the other hand, identiﬁes diﬃcult examples with boosting preprocessing and uses them to generate synthetic data [37].”
AdaBoost with SVM-based component classifiers (2008, methodology): “In [21], Boosting combined with data generation was used to solve imbalanced problems.”
Training and assessing classification rules with imbalanced data (2012, background): “Similarly, boosting is combined with data generation in Guo and Viktor (2004) and Mease et al. (2007).”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “Following this idea, many approaches have been developed by modifying the standard boosting weight-update mechanism in order to improve the performance on the minority class and the small disjuncts [30,44,61,69,74,112,117,122].”
Diversity analysis on imbalanced data sets by using ensemble models (2009, methodology): “DataBoost has a different goal – improve performance of minority class without sacrificing the performance of majority class.”
A novel ensemble method for classifying imbalanced data (2015, methodology): “Moreover, Bagging [27] and Boosting [28] based ensemble methods are another widely used methods to deal with imbalanced problems [15,16,29,30].”
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].”
Handling imbalanced datasets: A review (2006, background): “Clever re-sampling and combination methods can do quite more than cost-sensitive learning as they can provide new information or eliminate redundant information for the learning algorithm, as shown by [5], [6], [24], [14], [2].”
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, neutral): “To solve the problem of imbalanced data-sets there are two main types of solutions: (1) Solutions at the data level [2,4,18]: This kind of solution consists of balancing the class distribution by oversampling the minority class (positive instances) or under-sampling the majority class (negative inst”
A Review of Generalized Zero-Shot Learning Methods (2020, methodology): “Generative-based methods are originally designed to generate examples from the existing ones to train DL models [133] and compensate the imbalanced classiﬁcation problems [134].”
Boosting support vector machines for imbalanced data sets (2008, methodology): “Boosting and other ensemble learning methods have recently been used with great success on many applications [8,17,19].”
Editorial: special issue on learning from imbalanced data sets (2004, methodology): “Guo and Viktor [21] propose another technique that modifies the boosting procedure — DataBoost.”