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- W2993673073 abstract "This paper studies a combination of feature selection and ensemble learning to address the feature redundancy and class imbalance problems in software fault prediction. Also, a deep learning model is used to generate deep representation from defect data to improve the performance of fault prediction models. The proposed method, GFsSDAEsTSE, is evaluated on 12 NASA datasets, and the results show that GFsSDAEsTSE outperforms state-of-the-art methods in both small and large datasets." @default.
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- W2993673073 date "2019-10-01" @default.
- W2993673073 modified "2023-10-01" @default.
- W2993673073 title "Combining feature selection, feature learning and ensemble learning for software fault prediction" @default.
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- W2993673073 doi "https://doi.org/10.1109/kse.2019.8919292" @default.
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