@article{(Open Science Index):https://publications.waset.org/pdf/596,
	  title     = {Ensembling Adaptively Constructed Polynomial Regression Models},
	  author    = {Gints Jekabsons},
	  country	= {},
	  institution	= {},
	  abstract     = {The approach of subset selection in polynomial
regression model building 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 our research we consider a potentially more efficient approach –
Adaptive Basis Function Construction (ABFC). It lets the model
building method itself construct the basis functions necessary for
creating a model of arbitrary complexity with adequate predictive
performance. However, there are two issues that to some extent
plague the methods of both the subset selection and the ABFC,
especially when working with relatively small data samples: the
selection bias and the selection instability. We try to correct these
issues by model post-evaluation using Cross-Validation and model
ensembling. To evaluate the proposed method, we empirically
compare it to ABFC methods without ensembling, to a widely used
method of subset selection, as well as to some other well-known
regression modeling methods, using publicly available data sets.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {2},
	  number    = {2},
	  year      = {2008},
	  pages     = {113 - 118},
	  ee        = {https://publications.waset.org/pdf/596},
	  url   	= {https://publications.waset.org/vol/14},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 14, 2008},
	}