Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Agarwal, A.Subject: search.filters.subject.Feature Selection

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    Comparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kelkar, S.; Vishvasrao, S.P.; Agarwal, A.; Rajput, C.; Mohan, B.R.; Das, M.
    Software reliability is a crucial aspect of software quality. In this paper, we aim to explore the application of Gray Wolf Optimization (GWO) for feature selection and classification on various software dataset, such as KC1, JM1, and PC5. We compare the performance of Machine Learning models (Random Forest, Decision Tree, Support Vector Machine, XGBoost and Neural Networks) with and without GWO-based feature selection. Our results demonstrate the effectiveness of GWO in enhancing the accuracy of software reliability analysis. Or Math in Paper Title or Abstract. © 2024 IEEE.