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Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering.

, and . Int. J. Softw. Innov., 2 (4): 60-71 (2014)

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Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering., and . Int. J. Softw. Innov., 2 (4): 60-71 (2014)Software Fault Prediction Based on Improved Fuzzy Clustering., and . DCAI, volume 290 of Advances in Intelligent Systems and Computing, page 165-172. Springer, (2014)Increasing the Accuracy of Software Fault Prediction Using Majority Ranking Fuzzy Clustering., and . Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, volume 569 of Studies in Computational Intelligence, Springer, (2015)Software fault prediction using BP-based crisp artificial neural networks., , and . Int. J. Intell. Inf. Database Syst., 9 (1): 15-31 (2015)Fault prediction by utilizing self-organizing Map and Threshold., , and . ICCSCE, page 465-470. IEEE, (2013)Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks., , and . J. Ambient Intell. Humaniz. Comput., 11 (11): 5581-5609 (2020)RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks., , , , , and . IEEE Access, (2022)A survey on software fault detection based on different prediction approaches., and . Vietnam. J. Comput. Sci., 1 (2): 79-95 (2014)Improving software fault prediction in imbalanced datasets using the under-sampling approach., , , and . ICSCA, page 41-47. ACM, (2022)Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap., , , and . J. Exp. Theor. Artif. Intell., 35 (2): 171-206 (February 2023)