Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector...

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Autores principales:	Najm Assia, Zakrani Abdelali, Marzak Abdelaziz
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/96a67bb2bb4f4e85968a86c69044db49
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spelling	oai:doaj.org-article:96a67bb2bb4f4e85968a86c69044db492021-12-02T17:13:38ZOptainet-based technique for SVR feature selection and parameters optimization for software cost prediction2261-236X10.1051/matecconf/202134801002https://doaj.org/article/96a67bb2bb4f4e85968a86c69044db492021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/17/matecconf_inbes2021_01002.pdfhttps://doaj.org/toc/2261-236XThe software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.Najm AssiaZakrani AbdelaliMarzak AbdelazizEDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 348, p 01002 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Najm Assia Zakrani Abdelali Marzak Abdelaziz Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction
description	The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.
format	article
author	Najm Assia Zakrani Abdelali Marzak Abdelaziz
author_facet	Najm Assia Zakrani Abdelali Marzak Abdelaziz
author_sort	Najm Assia
title	Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction
title_short	Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction
title_full	Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction
title_fullStr	Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction
title_full_unstemmed	Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction
title_sort	optainet-based technique for svr feature selection and parameters optimization for software cost prediction
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/96a67bb2bb4f4e85968a86c69044db49
work_keys_str_mv	AT najmassia optainetbasedtechniqueforsvrfeatureselectionandparametersoptimizationforsoftwarecostprediction AT zakraniabdelali optainetbasedtechniqueforsvrfeatureselectionandparametersoptimizationforsoftwarecostprediction AT marzakabdelaziz optainetbasedtechniqueforsvrfeatureselectionandparametersoptimizationforsoftwarecostprediction
_version_	1718381332651835392

Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

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