Transferring Knowledge by Prior Feature Sampling
Victor Eruhimov, Vladimir Martyanov, Aleksey Polovinkin;
JMLR W&P 4:135-147, 2008.
Abstract
The paper presents a novel method for transfer learning through
prior variable sampling. A set of problems defined in the same
feature space with similar dependencies of target on features is
considered. We suggest a method for learning a decision tree
ensemble on each of the problems by prior estimation of variable
importance on other problems in the set and using it for
regularizing model learning for a small amount of training
samples. The method is tested on several simulated and real
datasets. In particular, we apply our method for a set of time
series classification (TSC) problems. Our analysis demonstrates an
intriguing result: a model trained on several TSC problems can
learn a new problem with high accuracy from a low number of
samples.
