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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46290

Title: Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets
Authors: Chin-Teng Lin
Tsung-Yu Hsieh
Yu-Ting Liu
Yang-Yin Lin
Chieh-Ning Fang
Yu-Kai Wang
Gary Yen
Nikhil R. Pal
Chun-Hsiang Chuang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Class imbalance;adaptive subspace self-organizing maps;kernel;minority oversampling
Date: 2018-05
Issue Date: 2018-05-14T07:14:54Z
Publisher: IEEE Transactions on Knowledge and Data Engineering
Abstract: Abstract:
The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.
Relation: 30(5),pp. 950-962
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46290
Appears in Collections:[資訊工程學系] 期刊論文

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