Re: DATA MINING: Use of modern heuristics to transform and select regressors for linear modelling

On Aug 13, 2:08 pm, Shah <shahryar.rah...@xxxxxxxxx> wrote:
Hi,
I am working on a project that intends to investigate the
implementation of a modern heuristic (e.g. simulated annealing,
genetic algorithms or local search) to search through a space of
polynomial transformations and assign selections for a linear
regression.

I have read that standard statistical methods for finding suitable
transformations of regressors use hill-climbing algorithms to search
for the correct transformations for linear modelling. I have found
that alot of times techniques such as stepwise regression have been
used to select a subset of regressors using a greedy algorithm.

BUT when this technique is used on a more complex model these
algorithms would fail to reach a global optimum.

I would like to know if by adopting a heuristic technique it may be
possible to provide better results.

(Could anyone post any suggestions/possible reading material/anything
that has been done along the same lines)

Thanks,

If your purpose is to support statistical interpolation of values in
time series then I suggest you take a look at the following

http://www.eyrolles.com/Informatique/Livre/9781558608559/

Hope this helps...

.

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