Improvement of effort estimation accuracy in software projects using a feature selection approach
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has becom...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science and Research Branch,Islamic Azad University
2016-12-01
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| Series: | Journal of Advances in Computer Engineering and Technology |
| Subjects: | |
| Online Access: | http://jacet.srbiau.ac.ir/article_9711_01b473654af050d0af8df6bf5d61665c.pdf |
| _version_ | 1819209868743016448 |
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| author | Zahra Shahpar Vahid Khatibi Asma Tanavar Rahil Sarikhani |
| author_facet | Zahra Shahpar Vahid Khatibi Asma Tanavar Rahil Sarikhani |
| author_sort | Zahra Shahpar |
| collection | DOAJ |
| description | In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy. |
| first_indexed | 2024-12-23T06:02:07Z |
| format | Article |
| id | doaj.art-c5c370fdc8bb46f18aeef6993e4f1f0a |
| institution | Directory Open Access Journal |
| issn | 2423-4192 2423-4206 |
| language | English |
| last_indexed | 2024-12-23T06:02:07Z |
| publishDate | 2016-12-01 |
| publisher | Science and Research Branch,Islamic Azad University |
| record_format | Article |
| series | Journal of Advances in Computer Engineering and Technology |
| spelling | doaj.art-c5c370fdc8bb46f18aeef6993e4f1f0a2022-12-21T17:57:40ZengScience and Research Branch,Islamic Azad UniversityJournal of Advances in Computer Engineering and Technology2423-41922423-42062016-12-012431389711Improvement of effort estimation accuracy in software projects using a feature selection approachZahra Shahpar0Vahid Khatibi1Asma Tanavar2Rahil Sarikhani3Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman,Iran.Faculty Member of Islamic Azad University, Kerman Branch, Kerman,Iran.Department of Computer, Kerman Branch, Islamic Azad UniversityDepartment of Computer, Kerman Branch, Islamic Azad University, IranIn recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy.http://jacet.srbiau.ac.ir/article_9711_01b473654af050d0af8df6bf5d61665c.pdfdimensionality reductionFeature SelectionGenetic Algorithmsoftware effort estimation |
| spellingShingle | Zahra Shahpar Vahid Khatibi Asma Tanavar Rahil Sarikhani Improvement of effort estimation accuracy in software projects using a feature selection approach Journal of Advances in Computer Engineering and Technology dimensionality reduction Feature Selection Genetic Algorithm software effort estimation |
| title | Improvement of effort estimation accuracy in software projects using a feature selection approach |
| title_full | Improvement of effort estimation accuracy in software projects using a feature selection approach |
| title_fullStr | Improvement of effort estimation accuracy in software projects using a feature selection approach |
| title_full_unstemmed | Improvement of effort estimation accuracy in software projects using a feature selection approach |
| title_short | Improvement of effort estimation accuracy in software projects using a feature selection approach |
| title_sort | improvement of effort estimation accuracy in software projects using a feature selection approach |
| topic | dimensionality reduction Feature Selection Genetic Algorithm software effort estimation |
| url | http://jacet.srbiau.ac.ir/article_9711_01b473654af050d0af8df6bf5d61665c.pdf |
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