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• W4288045786 abstract "One key challenging problem in data mining and decision-making is to establish a decision support system based on unbalanced datasets. In this study, we propose a novel algorithm to handle unbalanced learning problems that integrates the advantages of Siamese convolutional neural networks (SCNN) and the online reweighted example (ORE) algorithm into a unified method. First, the SCNN model is established for learning and extracting deep feature features at different levels. Second, the ORE algorithm is used to address the problem of data with a class-imbalanced distribution. Compared with baseline approaches, the experimental results show that our proposed method substantially enhances the performance of both within-project defect prediction and cross-project defect prediction." @default.
• W4288045786 created "2022-07-27" @default.
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• W4288045786 date "2022-12-01" @default.
• W4288045786 modified "2023-09-22" @default.
• W4288045786 title "Siamese networks with an online reweighted example for imbalanced data learning" @default.
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• W4288045786 doi "https://doi.org/10.1016/j.patcog.2022.108947" @default.
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