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Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection
Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defec...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050160/ https://www.ncbi.nlm.nih.gov/pubmed/33880074 http://dx.doi.org/10.1007/s10586-021-03282-8 |
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author | Mustaqeem, Mohd. Saqib, Mohd. |
author_facet | Mustaqeem, Mohd. Saqib, Mohd. |
author_sort | Mustaqeem, Mohd. |
collection | PubMed |
description | Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defects with more accuracy, reduce time and space complexities. All the previous research conducted on the data without feature reduction lead to the curse of dimensionality. We brought up a machine learning hybrid approach by combining Principal component Analysis (PCA) and Support vector machines (SVM) to overcome the ongoing problem. We have employed PROMISE (CM1: 344 observations, KC1: 2109 observations) data from the directory of NASA to conduct our research. We split the dataset into training (CM1: 240 observations, KC1: 1476 observations) dataset and testing (CM1: 104 observations, KC1: 633 observations) datasets. Using PCA, we find the principal components for feature optimization which reduce the time complexity. Then, we applied SVM for classification due to very native qualities over traditional and conventional methods. We also employed the GridSearchCV method for hyperparameter tuning. In the proposed hybrid model we have found better accuracy (CM1: 95.2%, KC1: 86.6%) than other methods. The proposed model also presents higher evaluation in the terms of other criteria. As a limitation, the only problem with SVM is there is no probabilistic explanation for classification which may very rigid towards classifications. In the future, some other method may also introduce which can overcome this limitation and keep a soft probabilistic based margin for classification on the optimal hyperplane. |
format | Online Article Text |
id | pubmed-8050160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-80501602021-04-16 Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection Mustaqeem, Mohd. Saqib, Mohd. Cluster Comput Article Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defects with more accuracy, reduce time and space complexities. All the previous research conducted on the data without feature reduction lead to the curse of dimensionality. We brought up a machine learning hybrid approach by combining Principal component Analysis (PCA) and Support vector machines (SVM) to overcome the ongoing problem. We have employed PROMISE (CM1: 344 observations, KC1: 2109 observations) data from the directory of NASA to conduct our research. We split the dataset into training (CM1: 240 observations, KC1: 1476 observations) dataset and testing (CM1: 104 observations, KC1: 633 observations) datasets. Using PCA, we find the principal components for feature optimization which reduce the time complexity. Then, we applied SVM for classification due to very native qualities over traditional and conventional methods. We also employed the GridSearchCV method for hyperparameter tuning. In the proposed hybrid model we have found better accuracy (CM1: 95.2%, KC1: 86.6%) than other methods. The proposed model also presents higher evaluation in the terms of other criteria. As a limitation, the only problem with SVM is there is no probabilistic explanation for classification which may very rigid towards classifications. In the future, some other method may also introduce which can overcome this limitation and keep a soft probabilistic based margin for classification on the optimal hyperplane. Springer US 2021-04-16 2021 /pmc/articles/PMC8050160/ /pubmed/33880074 http://dx.doi.org/10.1007/s10586-021-03282-8 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Mustaqeem, Mohd. Saqib, Mohd. Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection |
title | Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection |
title_full | Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection |
title_fullStr | Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection |
title_full_unstemmed | Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection |
title_short | Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection |
title_sort | principal component based support vector machine (pc-svm): a hybrid technique for software defect detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050160/ https://www.ncbi.nlm.nih.gov/pubmed/33880074 http://dx.doi.org/10.1007/s10586-021-03282-8 |
work_keys_str_mv | AT mustaqeemmohd principalcomponentbasedsupportvectormachinepcsvmahybridtechniqueforsoftwaredefectdetection AT saqibmohd principalcomponentbasedsupportvectormachinepcsvmahybridtechniqueforsoftwaredefectdetection |