Class Imbalance in Software Fault Prediction Data Set

[1]  Aleix M. Martínez,et al.  Subclass discriminant analysis , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Xiao Liu,et al.  An empirical study on software defect prediction with a simplified metric set , 2014, Inf. Softw. Technol..

[3]  Taeho Jo,et al.  A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..

[4]  Damminda Alahakoon,et al.  Minority report in fraud detection: classification of skewed data , 2004, SKDD.

[5]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[6]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[7]  Simon Fong,et al.  An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets , 2013, DaEng.

[8]  Baowen Xu,et al.  An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems , 2017, IEEE Transactions on Software Engineering.

[9]  Mark Johnston,et al.  Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  R. Bharat Rao,et al.  Data mining for improved cardiac care , 2006, SKDD.

[11]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Yuming Zhou,et al.  A novel ensemble method for classifying imbalanced data , 2015, Pattern Recognit..

[13]  Yue-Shi Lee,et al.  Cluster-based under-sampling approaches for imbalanced data distributions , 2009, Expert Syst. Appl..

[14]  Yunqian Ma,et al.  Imbalanced Learning: Foundations, Algorithms, and Applications , 2013 .

[15]  Salvatore J. Stolfo,et al.  Distributed data mining in credit card fraud detection , 1999, IEEE Intell. Syst..

[16]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[17]  Kazuyuki Murase,et al.  A Novel Synthetic Minority Oversampling Technique for Imbalanced Data Set Learning , 2011, ICONIP.

[18]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[19]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[20]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[21]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[22]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[23]  Xin Yao,et al.  Multiclass Imbalance Problems: Analysis and Potential Solutions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[25]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[26]  Salvatore J. Stolfo,et al.  Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection , 1998, KDD.

[27]  Wu Qingfeng,et al.  An empirical study on ensemble selection for class-imbalance data sets , 2010, 2010 5th International Conference on Computer Science & Education.