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

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, Received on Mon Aug 13 2007 - 07:08:36 CDT