Dual autoencoders features for imbalance classification problem

Many classification problems encountered in real-world applications exhibit a profile of imbalanced data. Current methods depend on data resampling. In fact, if the feature set provides a clear decision boundary, resampling may not be needed to solve the imbalanced classification problem. Therefore,...

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Bibliographic Details
Published in:Pattern recognition : the journal of the Pattern Recognition Society, Vol. 60 (2016), p. 875-889
Main Author: Ng, Wing W.Y.
Other Involved Persons: Zeng, Guangjun ; Zhang, Jiangjun ; Yeung, Daniel S. ; Pedrycz, Witold
Format: electronic Article
Physical Description:Online-Ressource
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