Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem
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...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/85295 http://hdl.handle.net/10220/12899 |
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| Institution: | Nanyang Technological University |
| Language: | English |