research-article

A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation

Authors:

Petrônio L. Braga,

Adriano L. I. Oliveira,

Silvio R. L. MeiraAuthors Info & Claims

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

Pages 1788 - 1792

https://doi.org/10.1145/1363686.1364116

Published: 16 March 2008 Publication History

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Abstract

The precision of the estimation of the effort of software projects is very important for the competitiveness of software companies. Machine learning methods have recently been applied for this task, included methods based on support vector regression (SVR). This paper proposes and investigates the use of a genetic algorithm approach for simultaneously (1) select an optimal feature subset and (2) optimize SVR parameters, aiming to improve the precision of the software effort estimates. We report on experiments carried out using two datasets of software projects. In both datasets, the simulations have shown that the proposed GA-based approach was able to improve substantially the performance of SVR and outperform some recent results reported in the literature.

References

[1]

P. L. Braga, A. L. I. Oliveira, G. H. T. Ribeiro, and S. R. L. Meira. Bagging predictors for estimation of software project effort. IEEE International Joint Conference on Neural Networks, IJCNN', 2007.

Crossref

Google Scholar

[2]

P. L. Braga, A. L. I. Oliveira, G. H. T. Ribeiro, and S. R. L. Meira. Software effort estimation using machine learning techniques with robust confidence intervals. IEEE International Conference on Tools with Artificial Intelligence, ICTAI, 2007.

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Google Scholar

[3]

C. J. Burgess and M. Lefley. Can genetic programming improve software effort estimation? A comparative evaluation. Information & Software Technology, 43(14):863--873, 2001.

Crossref

Google Scholar

[4]

C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273--297, 1995.

Crossref

Google Scholar

[5]

A. P. Engelbrecht. Fundamentals of Computational Swarm Intelligence. John Wiley & Sons, NJ, Jan. 2006.

Digital Library

Google Scholar

[6]

C.-L. Huang and C.-J. Wang. A ga-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31(2):231--240, 2006.

Crossref

Google Scholar

[7]

A. L. I. Oliveira. Estimation of software project effort with support vector regression. Neurocomputing, 69(13--15):1749--1753, 2006.

Digital Library

Google Scholar

[8]

M. Shin and A. L. Goel. Empirical data modeling in software engineering using radical basis functions. IEEE Trans. Software Eng, 26(6):567--576, 2000.

Digital Library

Google Scholar

[9]

A. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199--222, 2004.

Digital Library

Google Scholar

[10]

Standish. Project success rates improved over 10 years. Technical report, Standish Group, http//www.softwaremag.com/L.cfm? Doc=newsletter/2004-01-15/Standish, 2004.

Google Scholar

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Catak TDurdu PIlhan Omurca S(2024)Enhancing Agile Effort Estimation: An NLP Approach for Software Requirements Analysis2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA61326.2024.10550870(1-8)Online publication date: 23-May-2024
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A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation

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Published In

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

March 2008

2586 pages

ISBN:9781595937537

DOI:10.1145/1363686

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Hisham M. Haddad
Kennesaw State University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 March 2008

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SAC '08: The 2008 ACM Symposium on Applied Computing

March 16 - 20, 2008

Fortaleza, Ceara, Brazil

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Hichri AHajji MMansouri MNounou HBouzrara K(2024)Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systemsJournal of Engineering and Applied Science10.1186/s44147-023-00344-z71:1Online publication date: 5-Jan-2024
https://doi.org/10.1186/s44147-023-00344-z
Catak TDurdu PIlhan Omurca S(2024)Enhancing Agile Effort Estimation: An NLP Approach for Software Requirements Analysis2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA61326.2024.10550870(1-8)Online publication date: 23-May-2024
https://doi.org/10.1109/HORA61326.2024.10550870
Benzitouni MHannousse A(2024)Enhancing Intrusion Detection Systems Through Simultaneous Feature Selection and Hyperparameter Tuning13th International Conference on Information Systems and Advanced Technologies “ICISAT 2023”10.1007/978-3-031-60594-9_18(159-168)Online publication date: 31-Jul-2024
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GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

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GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

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The Improved Particle Swarm Optimization for Feature Selection of Support Vector Machine

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