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2007-2008
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| Funding |
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Hasler foundation (http://www.haslerstiftung.ch/)
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Model averaging approaches for credible classification
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A general problem in the design of any classifier is feature selection,
i.e., the identification of the supposedly optimal set of attributes; it
has been shown that using Bayesian Model Averaging (BMA) to
statistically aggregate the output of several classifiers, characterized
by different feature sets, leads to better accuracy than relying on a
single classifier, characterized by the supposedly bestfeature set. Yet,
currently there exist no BMA techique to aggregate credal classifiers.
The first goal of the project is hence the development of BMA for the
Naive Credal Classifier (NCC); this will allow for statistically
aggregating the output of different NCCs, characterized by different
feature sets. BMA for NCC aims hence at avoid the overfitting arising
from the choice of a single feature set (thanks to BMA) and at dealing
robustly with the specification of the prior of each single classifier
(thanks to credal approach). BMA for NCC is going to be developed in an
analytical way, i.e. its formulas will be exact and its computation will
be fast.
The second goal of the project is to address a general issue of BMA,
i.e. its need for the specification of a single, precise prior
distribution over the different classifiers (which actually represents
the a priori belief of the
investigator about the credibility of the different classifiers).
In fact, on small data sets, the classification
issued via BMA might be sensitive on the
specification of such prior. To overcome this drawback, we plan to extend
BMA itself to imprecise probabilities, thus developing Credal Model
Averaging (CMA). CMA will hence regard as feasible a set of priors over the models, rather than a
single prior. Eventually, the
developed methodologies will be extensively validated
on real world data sets.
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| People |
Giorgio Corani
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Coordinator
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Marco Zaffalon
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