Embedding Undersampling Rotation Forest for Imbalanced Problem
Joint Authors
Liu, Hongbing
Guo, Huaping
Diao, Xiaoyu
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-01
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space.
However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases.
This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set.
For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers.
With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class.
The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.
American Psychological Association (APA)
Guo, Huaping& Diao, Xiaoyu& Liu, Hongbing. 2018. Embedding Undersampling Rotation Forest for Imbalanced Problem. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1130821
Modern Language Association (MLA)
Guo, Huaping…[et al.]. Embedding Undersampling Rotation Forest for Imbalanced Problem. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1130821
American Medical Association (AMA)
Guo, Huaping& Diao, Xiaoyu& Liu, Hongbing. Embedding Undersampling Rotation Forest for Imbalanced Problem. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1130821
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130821