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Title:
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POLYNOMIAL REGRESSION MODELLING USING ADAPTIVE CONSTRUCTION OF BASIS FUNCTIONS |
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Author(s):
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Gints Jekabsons , Jurijs Lavendels |
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ISBN:
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978-972-8924-56-0 |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Polynomial regression, subset selection, basis function construction, heuristic search. |
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Type:
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Full Paper |
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First Page:
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269 |
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Last Page:
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276 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The approach of subset selection in regression modelling assumes that the chosen fixed full set of predefined basis
functions contains a subset that is sufficient to describe the target relation sufficiently well. However, in most cases the
necessary set of basis functions is not known and needs to be guessed a potentially non-trivial (and long) trial and error
process. In the paper we consider an adaptive basis function construction approach that in many problems has a potential
to be more efficient. It lets the modelling method itself construct the basis functions necessary for creating a regression
model of arbitrary complexity with adequate predictive performance. We also introduce an instance of the approach that
as a search strategy uses the floating search algorithm. To evaluate the proposed method, we compare it to other
regression modelling methods, including the well-known Sequential Forward Selection, on artificial and real world data. |
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