Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Garg:2012:ICMIC2,
  author =       "A. Garg and K. Tai",
  title =        "Comparison of regression analysis, Artificial Neural
                 Network and genetic programming in Handling the
                 multicollinearity problem",
  booktitle =    "Proceedings of International Conference on Modelling,
                 Identification Control (ICMIC 2012)",
  year =         "2012",
  month =        "24-26 " # jun,
  pages =        "353--358",
  size =         "6 pages",
  address =      "Wuhan, China",
  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.",
  keywords =     "genetic algorithms, genetic programming,
                 Multicollinearity, Factor Analysis, Principal Component
                 Analysis, Artificial Neural Network",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6260224",
  notes =        "Also known as \cite{6260224}",
}

Genetic Programming entries for Akhil Garg Kang Tai

Citations