Genetic Programming Transforms in Linear Regression Situations
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Castillo:2010:GPTP,
-
author = "Flor Castillo and Arthur Kordon and Carlos Villa",
-
title = "Genetic Programming Transforms in Linear Regression
Situations",
-
booktitle = "Genetic Programming Theory and Practice VIII",
-
year = "2010",
-
editor = "Rick Riolo and Trent McConaghy and
Ekaterina Vladislavleva",
-
series = "Genetic and Evolutionary Computation",
-
volume = "8",
-
address = "Ann Arbor, USA",
-
month = "20-22 " # may,
-
publisher = "Springer",
-
chapter = "11",
-
pages = "175--194",
-
keywords = "genetic algorithms, genetic programming, Multiple
Linear Regression, multicollinearity, soft sensor",
-
isbn13 = "978-1-4419-7746-5",
-
URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
-
DOI = "doi:10.1007/978-1-4419-7747-2_11",
-
abstract = "The chapter summarizes the use of Genetic Programming
(GP) inMultiple Linear Regression (MLR) to address
multicollinearity and Lack of Fit (LOF). The basis of
the proposed method is applying appropriate input
transforms (model respecification) that deal with these
issues while preserving the information content of the
original variables. The transforms are selected from
symbolic regression models with optimal trade-off
between accuracy of prediction and expressional
complexity, generated by multiobjective Pareto-front
GP. The chapter includes a comparative study of the
GP-generated transforms with Ridge Regression, a
variant of ordinary Multiple Linear Regression, which
has been a useful and commonly employed approach for
reducing multicollinearity. The advantages of
GP-generated model respecification are clearly defined
and demonstrated. Some recommendations for transforms
selection are given as well. The application benefits
of the proposed approach are illustrated with a real
industrial application in one of the broadest empirical
modeling areas in manufacturing - robust inferential
sensors. The chapter contributes to increasing the
awareness of the potential of GP in statistical model
building by MLR.",
-
notes = "part of \cite{Riolo:2010:GPTP}",
- }
Genetic Programming entries for
Flor A Castillo
Arthur K Kordon
Carlos Villa
Citations