Using Class Imbalance Learning for Software Defect Prediction

By Shuo Wang, Xin Yao. IEEE Transactions on Reliability, 2013

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Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, background): “These make ofﬂine data-driven EAs more challenging than online data-driven EAs, particularly when the data is imbalanced [41]–[43], noisy [44], time-varying [45], or heterogeneous [46].”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “[146] tune the settings of code clone detection techniques.”
Learning in Nonstationary Environments: A Survey (2015, background): “22 IEEE CompuTaTIonal InTEllIgEnCE magazInE | novEmbEr 2015 More recent works have extended these concepts to online algorithms, such as those in [108], [109].”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “that were previously used in the literature [18], [20], [21], [36], [37], [38], [39], [40], [41], [42].”
Online Defect Prediction for Imbalanced Data (2015, methodology): “, oversampling [50], undersampling [38], SMOTE [30], SMOTEBoost [51], negative correlation learning [52], cost-sensitive learning [48], data cleansing [53], and coding based ensemble learning [46].”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, methodology): “Wu et al. (2014) proposed Random Forest for imbalanced text categorization and applied on three benchmark datasets.”