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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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