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Noise-robust oversampling for imbalanced data classification
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DOI：10.1016/j.patcog.2022.109008.png)
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Abstract
En 中文
The class imbalance problem is characterized by an unequal data distribution in which majority classes have a greater number of data samples than minority classes. Oversampling methods generate samples for minority classes to balance the data distribution. However, the generated minority samples may over-lap with majority samples, resulting in noise. In this paper, we propose a noise-robust oversampling algorithm for mixed-type and multi-class imbalanced data. Our proposed noise-robust designs include an algorithm to eliminate noise within clusters of data samples, adaptive embedding to generate sam-ples safely, and a safe boundary for enlarging class boundaries. The heterogeneous distance metric and adapted decomposition strategy render our noise-robust algorithm suitable for mixed-type and multi -class imbalanced data. Experimental results on 20 benchmark datasets demonstrate the effectiveness of the proposed algorithm. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Imbalanced learning
Classification
Clustering
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