Software cost estimation using computational intelligence techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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