Indian Journal of Science and Technology
Year: 2022, Volume: 15, Issue: 6, Pages: 237-242
K J Eldho1*
1Assistant Professor, Department of Computer Science, Mary Matha Govt Aided Arts & Science College, Mananthavady, Kerala
Email: [email protected]
Received Date:25 November 2021, Accepted Date:23 January 2022, Published Date:16 February 2022
Objectives: To propose a suitable imbalanced data classification model to split the dataset into two new datasets and to test the created imbalanced dataset by the prediction models. Methods: The imbalance defect data sets are taken from the PROMISE library and used for the performance evaluation. The results clearly demonstrate that the performance of three existing prediction classifier models, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Back Propagation (BPN), is very susceptible in terms of unbalance of classification, while Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are more stable. Findings: The outcome of this research reveals that applied SVM and ELM machine learning models improves the performance in defect prediction and records 29% more than KNN, and 19% more than NB and BPN. Novelty: According to the findings of a comprehensive study, the proposed machine learning new classification imbalance impact analysis method outperforms the existing ones in order to transform the original imbalance data set into a new data set with an increasing imbalance rate and be able to select models to evaluate different predictions on the new data set.
Keywords: Software Fault Prediction Model; Imbalance Problem Classification; Artificial Intelligence; Smart Debugging; Unbalanced Classification
© 2022 Eldho. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Published By Indian Society for Education and Environment (iSee)
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