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cost-Sensitive Dual-Stream Residual Networks for Imbalanced Classification

Computers, Materials & Continua 2024年第9期80卷 4243-4261页

作者： Congcong Ma Jiaqi Mi Wanlin Gao Sha Tao College of Information and Electrical Engineering China Agricultural UniversityBeijing100083China Key Laboratory of Agricultural Informatization Standardization Ministry of Agriculture and Rural AffairsChina Agricultural UniversityBeijing100083China

Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different *** task is prevalent in practical scenarios such as industrial fault diagnosis,network intrusion detection,cancer detection,*** imbalanced classification tasks,the focus is typically on achieving high recognition accuracy for the minority ***,due to the challenges presented by imbalanced multi-class datasets,such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries,existing methods often do not perform well in multi-class imbalanced data classification tasks,particularly in terms of recognizing minority classes with high ***,this paper proposes a multi-class imbalanced data classification method called CSDSResNet,which is based on a cost-sensitive dualstream residual ***,to address the issue of limited samples in the minority class within imbalanced datasets,a dual-stream residual network backbone structure is designed to enhance the model’s feature extraction ***,considering the complexities arising fromimbalanced inter-class sample quantities and imbalanced inter-class overlapping boundaries in multi-class imbalanced datasets,a unique cost-sensitive loss function is *** loss function places more emphasis on the minority class and the challenging classes with high interclass similarity,thereby improving the model’s classification ***,the effectiveness and generalization of the proposed method,CSDSResNet,are evaluated on two datasets:‘DryBeans’and‘Electric Motor Defects’.The experimental results demonstrate that CSDSResNet achieves the best performance on imbalanced datasets,with macro_F1-score values improving by 2.9%and 1.9%on the two datasets compared to current state-of-the-art classification methods,***,it achieves the highest precision in single-class r

关键词： Deep learning imbalanced data classification fault diagnosis cost-sensitivity

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