Noise-robust oversampling for imbalanced data classification

•Propose three noise-robust mechanisms to address the noise generation problem in classic oversampling algorithms: adopting an advanced clustering algorithm, designing adaptive embedding to generate samples, and implementing a safe boundary to enlarge class boundaries.•Propose the heterogeneous dist...

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Bibliographic Details
Published in:Pattern recognition Vol. 133; p. 109008
Main Authors: Liu, Yongxu, Liu, Yan, Yu, Bruce X.B., Zhong, Shenghua, Hu, Zhejing
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2023
Subjects:
Classification
Clustering
Imbalanced learning
Imbalanced learning
Clustering
Classification
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:•Propose three noise-robust mechanisms to address the noise generation problem in classic oversampling algorithms: adopting an advanced clustering algorithm, designing adaptive embedding to generate samples, and implementing a safe boundary to enlarge class boundaries.•Propose the heterogeneous distance metric to better cluster mixed-type data along with dedicated approaches to avoid generating groundless samples with categorical variables.•Adapted decomposition strategy extends solution for binary imbalanced data to the multi-class setting. Moreover, better placement of new samples are provided.•Experiments on the standard datasets validate the effectiveness of the proposed data. 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 overlap 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 samples 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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2022.109008