Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data
By Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, 2011
321 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/408b3539cad0d5ca5f74ab60d4ed0b290f133a84
How researchers see this paper
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In Artificial Intelligence
- Francisco Herrera
- Alberto Fernández
- Salvador Garcia
- Victoria López
- M. J. Jesús
- Mikel Galar
- Vasile Palade
- Rohitash Chandra
In Electrical and Electronic Engineering
- Francisco Herrera
- Salvador Garcia
- M. J. Jesús
- Rohitash Chandra
- Azal Ahmad Khan
- Omkar Chaudhari
- Alberto Fernández
- Victoria López
Citations in the corpus
- A novel ensemble method for classifying imbalanced data (2015, methodology): “As the external methods have the advantage of independence on the underlying classification algorithms and our method could be regarded as a kind of external method, in this section we will pay more attention to the external methods, including the sampling methods [11, 13, 21, 55, 56, 57, 58, 59], B”
- Learning From Imbalanced Data (2019, background): “Khoshgoftaar et al. (2011) analyzed the relation between noise and imbalanced data.”
- A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Khoshgoftaar et al. [59] compared boosting techniques SMOTEBoost, and RUSBoost, against bagging techniques known as exactly balanced bagging and roughly balanced bagging and found that the bagging techniques performed better for imbalanced data with noise.”
- Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013, background): “A large number of approaches have been proposed to deal with the two-class imbalance problem, both for standard learning algorithms and for ensemble techniques [26,39,51].”
- An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, background): “Additionally, in [75], the authors presented a similar study on the significance of noise and imbalance data using bagging and boosting techniques.”