1. 1] S. Wang and X. Yao, "Using class imbalance learn-ing for software defect prediction," IEEE Trans. Reliab., vol. 62, no. 2, pp. 434-443, 2013. [
DOI:10.1109/TR.2013.2259203]
2. [2] B.-J. Park, S.-K. Oh, and W. Pedrycz, "The design of polynomial function-based neural network predictors for detection of software defects," Inf. Sci. (Ny)., vol. 229, pp. 40-57, 2013. [
DOI:10.1016/j.ins.2011.01.026]
3. [3] P. C. Pendharkar, "Exhaustive and heuristic search approaches for learning a software defect predic-tion model," Eng. Appl. Artif. Intell., vol. 23, no. 1, pp. 34-40, 2010. [
DOI:10.1016/j.engappai.2009.10.001]
4. [4] J. Zheng, "Cost-sensitive boosting neural networks for software defect prediction," Expert Syst. Appl., vol. 37, no. 6, pp. 4537-4543, 2010. [
DOI:10.1016/j.eswa.2009.12.056]
5. [5] Mahdizadeh Mahboubeh, Eftekhari Mahdi. "A new fuzzy rules weighting approach based on Genetic Programming for imbalanced classifica-tion" . JSDP., no. 11 (2), pp.111-125, 2015
6. [6] Z. Yan, X. Chen, and P. Guo, "Software defect prediction using fuzzy support vector regression," Adv. Neural Networks-ISNN 2010, pp. 17-24, 2010. [
DOI:10.1007/978-3-642-13318-3_3]
7. [7] A. K. Pandey and N. K. Goyal, "A fuzzy model for early software fault prediction using process matur-ity and software metrics," Int. J. Electron. Eng., vol. 1, no. 2, pp. 239-245, 2009.
8. [8] S. Di Martino, F. Ferrucci, C. Gravino, and F. Sarro, "A genetic algorithm to configure support vector machines for predicting fault-prone com-ponents," in International Conference on Product Focused Software Process Improvement, 2011, pp. 247-261. [
DOI:10.1007/978-3-642-21843-9_20]
9. [9] P. S. Sandhu, S. Khullar, S. Singh, S. K. Bains, M. Kaur, and G. Singh, "A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm," World Acad. Sci. Eng. Technol., vol. 72, 2010.
10. [10] M. M. Rosli, N. H. I. Teo, N. S. M. Yusop, and N. S. Mohammad, "The design of a software fault prone application using evolutionary algorithm," in Open Systems (ICOS), 2011 IEEE Conference on, 2011, pp. 338-343. [
DOI:10.1109/ICOS.2011.6079246]
11. [11] M.-Y. Chen, "A hybrid ANFIS model for busi-ness failure prediction utilizing particle swarm optimization and subtractive clustering," Inf. Sci. (Ny)., vol. 220, pp. 180-195, 2013. [
DOI:10.1016/j.ins.2011.09.013]
12. [12] M. E. R. Bezerra, A. L. I. Oliveira, and S. R. L. Meira, "A constructive rbf neural network for es-timating the probability of defects in software modules," in Neural Networks, 2007. IJCNN 2007. International Joint Conference on, 2007, pp. 2869-2874. [
DOI:10.1109/IJCNN.2007.4371415]
13. [13] M. E. R. Bezerra, A. L. I. Oliveiray, and P. J. L. Adeodatoz, "Predicting software defects: A cost-sensitive approach," in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, pp. 2515-2522. [
DOI:10.1109/ICSMC.2011.6084055]
14. [14] H. A. Al-Jamimi and L. Ghouti, "Efficient prediction of software fault proneness modules using support vector machines and probabilistic neural networks," in Software Engineering (MySEC), 2011 5th Malaysian Conference in, 2011, pp. 251-256. [
DOI:10.1109/MySEC.2011.6140679]
15. [15] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, "A possibilistic fuzzy c-means clustering algor-ithm," IEEE Trans. fuzzy Syst., vol. 13, no. 4, pp. 517-530, 2005. [
DOI:10.1109/TFUZZ.2004.840099]
16. [16] D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson, "Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics.," in EANN, 2009, vol. 2009, pp. 223-234. [
DOI:10.1007/978-3-642-03969-0_21]
17. [17] K. O. Elish and M. O. Elish, "Predicting defect-prone software modules using support vector machines," J. Syst. Softw., vol. 81, no. 5, pp. 649-660, 2008. [
DOI:10.1016/j.jss.2007.07.040]
18. [18] S. Wang, A. Mathew, Y. Chen, L. Xi, L. Ma, and J. Lee, "Empirical analysis of support vector machine ensemble classifiers," Expert Syst. Appl., vol. 36, no. 3, pp. 6466-6476, 2009. [
DOI:10.1016/j.eswa.2008.07.041]
19. [19] I. Gondra, "Applying machine learning to soft-ware fault-proneness prediction," J. Syst. Softw., vol. 81, no. 2, pp. 186-195, 2008. [
DOI:10.1016/j.jss.2007.05.035]
20. [20] S. R. Kannan, S. Ramathilagam, and P. C. Chung, "Effective fuzzy c-means clustering algorithms for data clustering problems," Expert Syst. Appl., vol. 39, no. 7, pp. 6292-6300, 2012. [
DOI:10.1016/j.eswa.2011.11.063]
21. [21] O. T. Yıldız, O. Aslan, and E. Alpaydın, "Mul-tivariate statistical tests for comparing classifica-tion algorithms," Lect Notes Comp Sci, vol. 6683, pp. 1-15, 2011. [
DOI:10.1007/978-3-642-25566-3_1]