Software Defect Prediction Method Based on Cost-Sensitive Random Forest

Zhao, Weidong; Zhang, Shengdong; Wang, Ming

doi:10.1007/978-3-031-03948-5_30

Cited by 1 publication

(2 citation statements)

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“…The data imbalance problem reduces the effectiveness of deep learning fault diagnosis [ 12 , 13 ]. To reduce the impact of imbalanced data on model performance of deep learning, data-level and algorithm-level approaches are proposed [ 14 , 15 , 16 , 17 , 18 ]. Among the data-level methods, oversampling and undersampling techniques are used to construct a balanced dataset [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the reliability of generated samples cannot be guaranteed when the quality of the real samples is poor. As the algorithm-level method, cost-sensitive learning can extract fault features directly from the imbalanced data by assigning class weights to increase the dominance of minority class samples in model training [ 18 ]. However, it is difficult to build an effective deep learning fault diagnosis model when the sample size of a single client is insufficient.…”

Section: Introductionmentioning

confidence: 99%

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Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Zhou

Yang

Wang

et al. 2023

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Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent.

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