id: 05960820
dt: a
an: 05960820
au: Serrurier, Mathieu; Prade, Henri
ti: Imprecise regression based on possibilistic likelihood.
so: Benferhat, Salem (ed.) et al., Scalable uncertainty management. 5th
    international conference, SUM 2011, Dayton, OH, USA, October 10‒13,
    2011. Proceedings. Berlin: Springer (ISBN 978-3-642-23962-5/pbk).
    Lecture Notes in Computer Science 6929. Lecture Notes in Artificial
    Intelligence, 447-459 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-23963-2_35
ab: Summary: Machine learning, and more specifically regression, usually
    focuses on the search for a precise model, when precise data are
    available. It is well-known that the model thus found may not exactly
    describe the target concept, due to the existence of learning biases.
    So, we are interested in a learning process that accounts also for the
    uncertainty around the predicted value which should not be illusionary
    precise. The goal of imprecise regression is to find a model that
    offers a good trade-off between faithfulness w.r.t. data and
    (meaningful) precision. The function that is learnt associates, to each
    input vector, a possibility distribution which represents a family of
    probability distributions. Based on this interpretation of a
    possibilistic distribution, we define the notion of possibilistic
    likelihood. Then, we propose a framework of imprecise regression based
    on the previous notion and a particle swarm optimization process. This
    approach takes advantage of the capability of triangular possibility
    distributions to approximate any unimodal probability distribution from
    above. We illustrate our approach with a generated dataset.
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