Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets.
IEEE Transactions on Knowledge and Data Engineering(2018)
摘要
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 ...
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关键词
Neurons,Kernel,Self-organizing feature maps,Data models,Machine learning algorithms,Algorithm design and analysis,Feature extraction
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