Software cost estimation using computational intelligence techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Pahariya:2009:NaBIC,

• author = "J. S. Pahariya and V. Ravi and M. Carr",
• title = "Software cost estimation using computational intelligence techniques",
• booktitle = "World Congress on Nature Biologically Inspired Computing, NaBIC 2009",
• year = "2009",
• month = dec,
• pages = "849--854",
• keywords = "genetic algorithms, genetic programming, SBSE, International Software Benchmarking Standards Group release 10 dataset, arithmetic mean, computational intelligence techniques, counter propagation neural network, data handling, dynamic evolving neuro-fuzzy inference system, geometric mean, group method, harmonic mean, linear ensembles, multilayer feedforward neural network, multiple linear regression, multivariate adaptive regression splines, polynomial regression, radial basis function neural network, recurrent architecture, regression tree, software cost estimation, support vector regression, ten-fold cross validation, tree net, data handling, fuzzy neural nets, fuzzy reasoning, geometry, radial basis function networks, regression analysis, software cost estimation, splines (mathematics), trees (mathematics)",
• DOI = "doi:10.1109/NABIC.2009.5393534",
• abstract = "This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for genetic programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz multiple linear regression, polynomial regression, support vector regression, classification and regression tree, multivariate adaptive regression splines, multilayer feedforward neural network, radial basis function neural network, counter propagation neural network, dynamic evolving neuro-fuzzy inference system, tree net, group method of data handling and genetic programming has been tested on the International Software Benchmarking Standards Group (ISBSG) release 10 dataset. Ten-fold cross validation is performed throughout the study. The results obtained from our experiments indicate that new recurrent architecture for genetic programming outperformed all the other techniques.",
• notes = "ISBSG-10 dataset Australia http://www.isbsg.org Also known as \cite{5393534}",

}

Genetic Programming entries for Janki Sharan Pahariya Vadlamani Ravi Shri Mahil Carr

Citations