# Improving Software Defect Prediction in Noisy Imbalanced Datasets
## 3 Materials and Methods
### Predictor Construction
[CrossRef]
* _[PERSON] et al. (2004)_ [PERSON]; [PERSON]; [PERSON] A study of the behavior of several methods for balancing machine learning training data. _ACM SIGKDD Explor. Newsl._**2004**, \(6\), 20-29. [CrossRef]
* _[PERSON] et al. (2005)_ [PERSON]; [PERSON]; [PERSON] Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. _Lect. Notes Comput. Sci._**2005**, _3644_, 878-887. [CrossRef]
* _[PERSON] et al. (2008)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of the International Joint Conference on Neural Networks, Hong Kong, China, 1-8 June 2008; pp. 1322-1328. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON], [PERSON] Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. _Inf. Sci._**2018**, _465_, 1-20. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON] Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning. _Expert Syst. Appl._**2017**, _82_, 40-52. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON] Gaussian-based SMOTE algorithm for solving skewed class distributions. _Int. J. Fuzzy Log. Intell. Syst._**2017**, _17_, 229-234. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] MWMOTE--Majority weighted minority oversampling technique for imbalanced data set learning. _IEEE Trans. Knowl. Data Eng._**2014**, _26_, 405-425. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] [PERSON] Snoring: A noise in defect prediction datasets. In Proceedings of the IEEE International Working Conference on Mining Software Repositories, Montreal, QC, Canada, 25-31 May 2019; pp. 63-67. [CrossRef]* [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON] MSMOTE: Improving classification performance when training data is imbalanced. In Proceedings of the 2 nd International Workshop on Computer Science and Engineering, WCSE 2009, Qingdao, China, 28-30 October 2009; Volume 2, pp. 13-17. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. _Inf. Sci._**2015**, _291_, 184-203. [CrossRef]
* [PERSON] (2017) [PERSON] [PERSON]; [PERSON] [PERSON] CCR: A combined cleaning and resampling algorithm for imbalanced data classification. _Int. J. Appl. Math. Comput. Sci._**2017**, _27_, 727-736. [CrossRef]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON] SMOTE-RSB *: A hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. _Knowl. Inf. Syst_**2012**, _33_, 245-265. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FREST-2T algorithm. _Eng. Appl. Artif. Intell._**2016**, _48_, 134-139. [CrossRef]
* [PERSON] and [PERSON] (2007) [PERSON] [PERSON]; [PERSON] Improving software quality prediction by noise filtering techniques. _J. Comput. Sci. Technol._**2007**, _22_, 387-396. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Software defect prediction using ensemble learning: A systematic literature review. _IEEE Access_**2021**, \(9\), 98754-98771. [CrossRef]
* [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] The Promise Repository of Empirical Software Engineering Data. Available online: [[http://promise.site.uottawa.ca/SERepository/](http://promise.site.uottawa.ca/SERepository/)]([http://promise.site.uottawa.ca/SERepository/](http://promise.site.uottawa.ca/SERepository/)) (accessed on 31 December 2007). * [PERSON] and [PERSON] (2013) [PERSON]; [PERSON] PROMISE and ISBSG software engineering data repositories: A survey.