Please use this identifier to cite or link to this item:
|Title:||Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem||Authors:||Garg, A.
|Issue Date:||2012||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 modeling 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 modeling methods are summarized in order to guide users about the proper techniques for tackling multicollinearity problems.||URI:||https://hdl.handle.net/10356/85295
|Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||MAE Conference Papers|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.