Optimizing SVR using Local Best PSO for Software Effort Estimation

Author

Dinda Novitasari, Imam Cholissodin, Wayan Firdaus Mahmudy

Abstract

Abstract. In the software industry world, it’s known to fulfill the tremendous demand. Therefore, estimating effort is needed to optimize the accuracy of the results, because it has the weakness in the personal analysis of experts who tend to be less objective. SVR is one of clever algorithm as machine learning methods that can be used. There are two problems when applying it; select features and find optimal parameter value. This paper proposed local best PSO-SVR to solve the problem. The result of experiment showed that the proposed model outperforms PSO-SVR and T-SVR in accuracy.
Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation

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References


T. Standish Group, “Chaos Manifesto 2013 Think Big, Act Small,” 2013.

R. Agarwal, M. Kumar, Yogesh, S. Mallick, R. M. Bharadwaj, and D. Anantwar, “Estimating Software Projects,” ACM SIGSOFT Software Engineering Notes, vol. 26, no. 4, pp. 60–67, 2001.

D. Zhang and J. J. Tsai, “Machine Learning and Software Engineering,” Software Quality Journal, vol. 11, no. 2, pp. 87–119, 2003.

K. Srinivasan and D. Fisher, “Machine Learning Approaches to Estimating Software Development Effort,” IEEE Transactions on Software Engineering, vol. 21, no. 2, pp. 126–137, 1995.

V. N. Vapnik, “An Overview of Statistical Learning Theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999.

H. Frohlich, O. Chapelle, and B. Scholkopf, “Feature Selection for Support Vector Machines by Means of Genetic Algorithm,” in Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, 2003, pp. 142–148.

Y. Guo, “An Integrated PSO for Parameter Determination and Feature Selection of SVR and Its Application in STLF,” in Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009, 2009, no. July, pp. 12–15.

W. Wang, Z. Xu, W. Lu, and X. Zhang, “Determination of The Spread Parameter in the Gaussian Kernel For Classification and Regression,” Neurocomputing, vol. 55, pp. 643–663, 2003.

P. Braga, A. Oliveira, and S. Meira, “A GA-based Feature Selection and Parameters Optimization for Support Vector Regression Applied to Software Effort Estimation,” in Proceedings of the 2008 ACM Symposium on Applied Computing, 2008, pp. 1788–1792.

G. Hu, L. Hu, H. Li, K. Li, and W. Liu, “Grid Resources Prediction With Support Vector Regression and Particle Swarm Optimization,” 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice, vol. 1, pp. 417–422, 2010.

M. Jiang, S. Jiang, L. Zhu, Y. Wang, W. Huang, and H. Zhang, “Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem,” Computational and Mathematical Methods in Medicine, vol. 2013, pp. 1–9, 2013.

A. P. Engelbrecht, Computational Intelligence: An Introduction, 2nd ed. West Sussex: John Wiley & Sons Ltd, 2007.

R. Mendes, J. Kennedy, and J. Neves, “The Fully Informed Particle Swarm: Simpler, Maybe better,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 204–210, 2004.

A. J. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004.

S. Vijayakumar and S. Wu, “Sequential Support Vector Classifiers and Regression,” in Proceedings of International Conference on Soft Computing (SOCO ‘99), 1999, vol. 619, pp. 610–619.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Neural Networks, 1995. Proceedings., IEEE International Conference on, 1995, vol. 4, pp. 1942–1948.

Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1998, pp. 69–73.

J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proceedings of the World Miulticonference on Systemics, Cybernetics and Informatics, 1997, pp. 4104–4109.

D. Novitasari, I. Cholissodin, and W. F. Mahmudy, “Hybridizing PSO with SA for Optimizing SVR Applied to Software Effort Estimation”, Telkomnika (Telecommunication Computing Electronics and Control), 2015, vol. 14, no. 1, pp. 245-253.

J. Sayyad Shirabad and T. J. Menzies, “The PROMISE Repository of Software Engineering Databases,” School of Information Technology and Engineering, University of Ottawa, Canada, 2005. [Online]. Available: http://promise.site.uottawa.ca/SERepository. [Accessed: 05-Mar-2015].

Mahmudy, WF 2014, 'Improved simulated annealing for optimization of vehicle routing problem with time windows (VRPTW)', Kursor, vol. 7, no. 3, pp. 109-116.




DOI: http://dx.doi.org/10.25126/jitecs.2016117