| Abstract: | We tackle the problem of authenticating high value Italian
wines through machine learning classification. The problem
is a seriuos one, since protection of high quality wines from
forgeries is worth several million of Euros each year. In a
previous work we have identified some base models (in particular
classifiers based on Bayesian network (BNC), multilayer
perceptron (MLP) and sequential minimal optimization
(SMO)) that well behave using unexpensive chemical analyses
of the interested wines. In the present paper, we investigate
the role of esemble learning in the construction of
more robust classifiers; results suggest that, while bagging
and boosting may significantly improve both BNC and MLP,
the SMO model is already very robust and efficient as a base
learner.We report on results concerning both cross validation
on two different datasets, as well as experiments with models
trained with the above datasets and tested with a dataset of potentially
fake wines; this has been synthesized from a generative
probabilistic model learned from real samples and expert
knowledge. Results open new opportunities in the wine fraud
detection activity, which is of primary importance in the figth
against the destabilization of the wine market worldwide. |
