> Is is possible to define a Stacking classifier with input classifiers that
> have been trained on distinct data sets? I’m basically trying to set up a
> stack of multiple Bayesian networks that have each been trained on a data
> set from a different data source, but it seems that the Stacking interface
> (at least in the KnowledgeFlow) only permits a single dataSet to be input to
> all of the classifiers; I can’t just generate them separately and hook them
> into a single Stacking object, apparently. Is there a way to do this?
No, each classifier in Weka (no matter whether simple or meta) takes
exactly *one* dataset as input. Using the API, you can fake the
training phase for some meta-classifiers (e.g., Vote). But this
doesn't work for Stacking, as it generates a meta-level dataset during
training.
Cheers, Peter
--
Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
http://www.cs.waikato.ac.nz/~fracpete/ Ph. +64 (7) 858-5174
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