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. 2021 Nov 12;16(11):e0259227.
doi: 10.1371/journal.pone.0259227. eCollection 2021.

An oversampling method for multi-class imbalanced data based on composite weights

Mingyang Deng  1   2 , Yingshi Guo  1 , Chang Wang  1 , Fuwei Wu  1
Affiliations

An oversampling method for multi-class imbalanced data based on composite weights

Mingyang Deng et al. PLoS One. .

Abstract

To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Multi-dimensional classification data.
Fig 2
Fig 2. Two-dimensional spatial classification data.
Fig 3
Fig 3. The classification oversampling algorithm flow chart.
Fig 4
Fig 4. Data sorting of oversampling for imbalanced data.
Fig 5
Fig 5. The 2-point sampling of oversampling for imbalanced data.
Fig 6
Fig 6. The 3-point sampling of oversampling for imbalanced data.
Fig 7
Fig 7. Inter-class sampling of oversampling for imbalanced data.
See this image and copyright information in PMC

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