Software cost estimation using computational intelligence techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Pahariya:2009:NaBIC,
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author = "J. S. Pahariya and V. Ravi and M. Carr",
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title = "Software cost estimation using computational
intelligence techniques",
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booktitle = "World Congress on Nature Biologically Inspired
Computing, NaBIC 2009",
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year = "2009",
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month = dec,
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pages = "849--854",
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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)",
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DOI = "doi:10.1109/NABIC.2009.5393534",
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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.",
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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