Imbalanced Data Evidential Classification with Composite Reliability

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Imbalanced Data Evidential Classification with Composite Reliability

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[1]TIAN Hongpeng,ZHANG Zhen,ZHANG Siyuan,et al.Imbalanced Data Evidential Classification with Composite Reliability[J].Journal of Zhengzhou University (Engineering Science),2023,44(04):22-28.[doi:10.13705/j.issn.1671-6833.2023.04.012]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 04 Page number: 22-28 Column: Public date: 2023-06-01

Title:: Imbalanced Data Evidential Classification with Composite Reliability

Author(s):: TIAN Hongpeng; ZHANG Zhen; ZHANG Siyuan; XIAO Zongrong; DONG Jiabing; School of Electrical and Information Engineering, Zhengzhou University, 450001, Zhengzhou, Henan

Keywords:: imbalanced data; classification; global reliability; local reliability; evidential reasoning

CLC:: TP181

DOI:: 10.13705/j.issn.1671-6833.2023.04.012

Abstract:: To address the problem that traditional classification models focus on majority class while ignoring minority class for classifying imbalanced data, An imbalanced data evidential classification method with composite reliability is proposed. This method improves the classification ability of the model for each imbalanced test sample by evaluating the global reliability and local reliability of the classification model. First, the method implements under sampling for majority class multiple times. The sampled subsets combine with minority class to form multiple training subsets. Multiple classification models are trained using these subsets. The maximum mean discrepancy measures the difference of data distribution before and after sampling, which can measure global reliability of the classification results obtained by classification models. Then, the local reliability of the classification result of the test sample in the training set is evaluated by using its nearest neighbors. The test sample and its nearest neighbors have similar data distribution and spatial structure. The smaller the deviation between the classification result of the classification model and the ground truth, the greater the local reliability of the classification result obtained by the classification model. Finally, under the framework of evidential reasoning, the global reliability and local reliability are combined as composite reliability factors to discount the classification results obtained from different classification models. Partial probability values are assigned to completely unknown classes to represent the uncertainty of classes. Dempster-Shafer (DS) rule is employed to fuse the classification results after multiple discounts for decision analysis. The experimental results show that the average FM and GM of the proposed method for the classification of 12 imbalanced data in KEEL and UCI database are 80. 18% and 87. 24%, respectively, which are 8. 1% and 4. 99% higher than those of other best imbalanced data classification methods, respectively. This proves the effectiveness of the proposed method in classifying imbalanced data.

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Last Update: 2023-06-30