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

From: Cimode <>
Date: Mon, 13 Aug 2007 05:17:12 -0700
Message-ID: <>

On Aug 13, 2:08 pm, Shah <> 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

Hope this helps... Received on Mon Aug 13 2007 - 14:17:12 CEST

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