Abstract
We use particle swarm optimization (PSO) to train the functional link artificial neural network (FLANN) for software effort prediction. The combined framework is known as PSO-FLANN. This framework exploits the global classification capability of PSO and FLANN’s complex nonlinear mapping between its input and output pattern space by using functional expansion. The Chebyshev polynomial has been used as choice of expansion in FLANN to exhaustively study the performance in three real time datasets. The simulation results show that it not only deals efficiently with noisy data but achieves improved accuracy in prediction.
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References
Araújo, R., de, A., Oliveira, A.L.I., Soares, S.: A shift-invariant morphological system for software development cost estimation. Expert Systems with Applications 38, 4162–4168 (2011)
Braga, P.L., Oliveira, A.L.I., Ribeiro, G.H.T., Meira, S.R.L.: Software effort estimation using machine learning techniques with robust confidence intervals. In: IEEE International Conference on Tools with Artificial Intelligence (ICTAI) (2007)
Dehuri, S., Roy, R., Cho, S.-B., Ghosh, A.: An Improved Swarm Optimized functional link artificial neural network (ISO-FLANN) for Clasification. J. Syst. Software 85(6) (2012)
Foss, T., Stensrud, E., Kitchenham, B., Myrtveit, I.: A simulation study of themodel evaluation criterion MMRE. IEEE Transactions on Software Engineering 29(11) (2003)
Huang, S.J., Chiu, N.H.: Optimization of analogy weights by genetic algorithm for software effort estimation. Information and Software Technology 48, 1034–1045 (2006)
Keung, J.W.: Theoretical Maximum Prediction Accuracy for Analogy-Based Software Cost Estimation. In: 15th Asia-Pacific Software Engineering Conference, pp. 495–502 (2008), http://ieeexplore.ieee.org/lpdocsepic03/wrapper.htm?arnumber=4724583
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Menzies, T.: The PROMISE Repository Of Software Engineering Databases. School of Information Technology and Engineering, University Of Ottawa, Canada (2006), http://promise.site.uottawa.ca/SERepository
Mendes, E., Watson, I., Triggs, C., Mosley, N., Counsell, S.: A Comparative Study of Cost Estimation Models for Web Hypermedia Applications. Empirical Software Engineering 8, 163–196 (2003)
Oliveira, A.L.I.: Estimation of software project effort with support vector regression. Neurocomputing 69(13-15), 1749–1753 (2006)
Shepperd, M., Kadoda, G.: Comparing Software Prediction Techniques using Simulation. IEEE Transaction on Software Engineering 27(11), 1014–1022 (2001)
Stensrud, E.: Alternative Approaches to Software Prediction of ERP Projects. Information and Software Technology 43(7), 413–423 (2001)
Stensrud, E., Foss, T., Kitchenham, B.A., Myrtveit, I.: An empirical validation of the relationship between the magnitude of relative error and project size. In: Proceedings of the IEEE 8th Metrics Symposium, pp. 3–12 (2002)
Tirimula Rao, B., Sameet, B., Kiran Swathi, G., Vikram Gupta, K., Raviteja, C., Sumana, S.: A Novel Neural Network approach for Software Cost Estimation Using Functional Link Artificial Neural Networks. International Journal of Computer Science and Network Security (IJCSNS) 9(6), 126–131 (2009)
Tirimula Rao, B., Dehuri, S., Mall, R.: Functional Link Artificial Neural Networks for Software Cost Estimation. International Journal of Applied Evolutionary Computation (IJAEC) 3(2), 62–82 (2012)
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Benala, T.R., Chinnababu, K., Mall, R., Dehuri, S. (2013). A Particle Swarm Optimized Functional Link Artificial Neural Network (PSO-FLANN) in Software Cost Estimation. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_8
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DOI: https://doi.org/10.1007/978-3-642-35314-7_8
Publisher Name: Springer, Berlin, Heidelberg
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