Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data
Joint Authors
Xia, Feng
Li, Fengqi
Yu, Chuang
Yang, Nanhai
Li, Guangming
Kaveh-Yazdy, Fatemeh
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-07-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Transductive graph-based semisupervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices.
Those methods propagate label information of labeled samples to neighbors through their edges in order to get the predicted labels of unlabeled samples.
Most popular semi-supervised learning approaches are sensitive to initial label distribution which happened in imbalanced labeled datasets.
The class boundary will be severely skewed by the majority classes in an imbalanced classification.
In this paper, we proposed a simple and effective approach to alleviate the unfavorable influence of imbalance problem by iteratively selecting a few unlabeled samples and adding them into the minority classes to form a balanced labeled dataset for the learning methods afterwards.
The experiments on UCI datasets and MNIST handwritten digits dataset showed that the proposed approach outperforms other existing state-of-art methods.
American Psychological Association (APA)
Li, Fengqi& Yu, Chuang& Yang, Nanhai& Xia, Feng& Li, Guangming& Kaveh-Yazdy, Fatemeh. 2013. Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1033399
Modern Language Association (MLA)
Li, Fengqi…[et al.]. Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data. The Scientific World Journal No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1033399
American Medical Association (AMA)
Li, Fengqi& Yu, Chuang& Yang, Nanhai& Xia, Feng& Li, Guangming& Kaveh-Yazdy, Fatemeh. Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1033399
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033399