Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Garg:2012:ICMIC2,
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author = "A. Garg and K. Tai",
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title = "Comparison of regression analysis, Artificial Neural
Network and genetic programming in Handling the
multicollinearity problem",
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booktitle = "Proceedings of International Conference on Modelling,
Identification Control (ICMIC 2012)",
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year = "2012",
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month = "24-26 " # jun,
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pages = "353--358",
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size = "6 pages",
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address = "Wuhan, China",
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abstract = "Highly correlated predictors in a data set give rise
to the multicollinearity problem and models derived
from them may lead to erroneous system analysis. An
appropriate predictor selection using variable
reduction methods and Factor Analysis (FA) can
eliminate this problem. These methods prove to be
commendable particularly when used in conjunction with
modelling methods that do not automate predictor
selection such as Artificial Neural Network (ANN),
Fuzzy Logic (FL), etc. The problem of severe
multicollinearity is studied using data involving the
estimation of fat content inside body. The purpose of
the study is to select the subset of predictors from
the set of highly correlated predictors. An attempt to
identify the relevant predictors is comprehensively
studied using Regression Analysis, Factor
Analysis-Artificial Neural Networks (FA-ANN) and
Genetic Programming (GP). The interpretation and
comparisons of modelling methods are summarised in
order to guide users about the proper techniques for
tackling multicollinearity problems.",
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keywords = "genetic algorithms, genetic programming,
Multicollinearity, Factor Analysis, Principal Component
Analysis, Artificial Neural Network",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6260224",
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notes = "Also known as \cite{6260224}",
- }
Genetic Programming entries for
Akhil Garg
Kang Tai
Citations