This is an aside to what your original question was, but as someone who has dealt with similar data in bioinformatics (gene expression, specifically) I think you should tread -very- carefully if you have such a small sample set and more dimensions than features. MLPs are already prone to overfit and both of those factors would make me inherently suspicious of the results. This sounds like an easy way to trick yourself into thinking you are making good predictions. Perhaps consider LASSO? Back to the original question, it is true that using a SVR in a stacking technique would add more parameters to your model, but it is likely an insignificant amount when compared to the MLPs themselves. Alternatively you may consider using LASSO using all of the MLPs (not just the top 10%) so you can learn which ones yield useful features for a meta-estimator instead of just selecting the top 10%. On Sat, Jan 7, 2017 at 4:01 PM, Thomas Evangelidis <tevang3@gmail.com> wrote:
On 8 January 2017 at 00:04, Jacob Schreiber <jmschreiber91@gmail.com> wrote:
If you have such a small number of observations (with a much higher feature space) then why do you think you can accurately train not just a single MLP, but an ensemble of them without overfitting dramatically?
Because the observations in the data set don't differ much between them. To be more specific, the data set consists of a congeneric series of organic molecules and the ebservation is their binding strength to a target protein. The idea was to train predictors that can predict the binding strenght of new molecules that belong to the same congeneric series. Therefore special care is taken to apply the predictors to the right domain of applicability. According to the literature, the same strategy has been followed in the past several times. The novelty of my approach stems from other factors that are irrelevant to this thread.
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Thomas Evangelidis
Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr
tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
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