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Resampling algorithms based on sample concatenation for imbalance learning
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DOI：10.1016/j.knosys.2022.108592.png)
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摘要
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Resampling is the widely used method for imbalance learning. Most existing resampling methods use various techniques in the original sample space to rebalance imbalanced datasets, but they may cause loss of valuable information or aggravate interclass overlap. In this paper, sample concatenation, i.e., concatenating two samples with the same labels into one sample, is introduced into imbalance learning, and a resampling algorithm based on sample concatenation (Re-SC) is proposed. Re-SC transforms an imbalanced training dataset in the original sample space into a concatenated dataset in a new sample space. In the transformation process, Re-SC considers both the distribution of the original dataset and that of the majority samples, thereby alleviating the loss of valuable samples and reducing the class overlapping region. Furthermore, an ensemble resampling algorithm based on sample concatenation (EnRe-SC) for imbalanced data is also proposed. EnRe-SC can reduce the negative effect of removing part of the samples from the majority class. Experiments were conducted on UCI and KEEL imbalanced datasets to evaluate the performance of the proposed methods. The results verify the effectiveness of the proposed methods. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Sample concatenation
Resampling
Class imbalance
Class overlap
Majority class
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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