ARCHIVES
Review Article
Optimized Fuzzy Classifier Approach for Predicting Defects
Kaushalya Thopate1
Diya Shaikh2
Muaz Shaikh3
Pushkraj Shahane4
1Asst Prof. Department of Computer Science Engineering Vishwakarma Institute of Technology, Pune, Maharashtra, India. 234 Students, Department of Computer Science Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India.
Published Online: November-December 2024
Pages: 07-10
Cite this article
No DOIReferences
1. Arshad, A., et al.: The empirical study of semi-supervised deep fuzzy C-mean clustering for software fault prediction. IEEE Access 6,
47047–54706 (2018).
2. Bal, P.R., Kumar, S.: WR-elm: Weighted regularization extreme learning machine for imbalance learning in software fault prediction.
IEEE Trans. Reliab. 69(4), 1355–1375
3. Borandag, E.: Software fault prediction using an RNN-based deep learning approach and ensemble machine learning techniques. Appl.
Sci. 13(3), 1639 (2023)
4. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Art. Intell. Res. 16,
321–357 (2002)
5. Desuky, A.S., Hussain, S.: An improved hybrid approach for handling class imbalance problem. Arab. J. Sci. Eng. 46, 3853–3864 (2021)
6. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003
47047–54706 (2018).
2. Bal, P.R., Kumar, S.: WR-elm: Weighted regularization extreme learning machine for imbalance learning in software fault prediction.
IEEE Trans. Reliab. 69(4), 1355–1375
3. Borandag, E.: Software fault prediction using an RNN-based deep learning approach and ensemble machine learning techniques. Appl.
Sci. 13(3), 1639 (2023)
4. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Art. Intell. Res. 16,
321–357 (2002)
5. Desuky, A.S., Hussain, S.: An improved hybrid approach for handling class imbalance problem. Arab. J. Sci. Eng. 46, 3853–3864 (2021)
6. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003
Related Articles
2024
Matrix Representation of Graph Theory in Hydrocarbons
2024
A Review of Development of Chemical Sensors
2024
Towards Detection and Attribution of Cyber Attacks in IoT Enabled Cyber-Physical Systems
2024
Implementation of Waste Management System
2024
To Study the Role of Forest –Based Industries in Promoting Trade
2024
