Oversampling the minority class in a multi-linear feature space for imbalanced data classification
Author:
Affiliation:
1. Graduate School of Information, Production and Systems; Waseda University; Kitakyushu-shi 808-0135 Japan
2. Shandong University of Science and Technology, 223 Daizong Street; Taian Shandong China
Publisher
Wiley
Subject
Electrical and Electronic Engineering
Link
http://onlinelibrary.wiley.com/wol1/doi/10.1002/tee.22715/fullpdf
Reference36 articles.
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2. Learning from imbalanced data;He;IEEE Transactions on Knowledge and Data Engineering,2009
3. SMOTE: Synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research,2002
4. Mine classification with imbalanced data;Williams;IEEE Geoscience and Remote Sensing Letters,2009
5. SVMs modeling for highly imbalanced classification;Tang;IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics),2009
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