Imbalanced Classification Based on Weighted Regularization Collaborative Representation

LI Yanting; WANG Shuai; JIN Junwei; MA Jiangtao; CHEN Xueyan; CHEN Junlong

doi:10.11999/JEIT220753

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2571-2579

LI Yanting, WANG Shuai, JIN Junwei, MA Jiangtao, CHEN Xueyan, CHEN Junlong. Imbalanced Classification Based on Weighted Regularization Collaborative Representation[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2571-2579. doi: 10.11999/JEIT220753

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LI Yanting, WANG Shuai, JIN Junwei, MA Jiangtao, CHEN Xueyan, CHEN Junlong. Imbalanced Classification Based on Weighted Regularization Collaborative Representation[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2571-2579. doi: 10.11999/JEIT220753

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Imbalanced Classification Based on Weighted Regularization Collaborative Representation

doi: 10.11999/JEIT220753

1.
College of Computer and Communication Engineering , Zhengzhou University of Light Industry, Zhengzhou 450001, China
2.
College of Artifificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China
3.
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China

Funds: The National Natural Science Foundation of China (62106233, 62106068), The Science and Technology Research Project of Henan Province (222102210058, 222102210027, 202102210122)

Received Date: 2022-06-27
Rev Recd Date: 2023-03-30

Available Online: 2023-03-31

Publish Date: 2023-07-10

Abstract

Abstract

Collaborative representation based classifier and its variants exhibit superior recognition performance in the field of pattern recognition. However, their success relies greatly on the balanced distribution of classes, and a highly imbalanced class distribution may seriously affect their effectiveness. To make up for this defect, this paper introduces the regularization term induced by the complemented subspace into the framework of collaborative representation model, which makes the improved regularization model more discriminative. Furthermore, in order to improve the recognition accuracy of the minority classes on imbalanced datasets, a class weight learning algorithm based on the nearest subspace is proposed according to the representation ability of each class of training samples. The algorithm obtains adaptively the weight of each class and can assign greater weights to the minority classes, so that the final classification results are more fair to the minority classes. The proposed model has a closed-form solution, which demonstrates its computational efficiency. Experimental results on authoritative public binary-class and multi-class imbalanced datasets show that the proposed method outperforms significantly other mainstream imbalanced classification algorithms.
- Imbalanced classification,
- Adaptive weight,
- Complemented subspace,
- Collaborative representation

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References(23)

References

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