id: 05255113
dt: a
an: 05255113
au: Van Assche, Anneleen; Blockeel, Hendrik
ti: Seeing the forest through the trees. Learning a comprehensible model from a
    first order ensemble.
so: Blockeel, Hendrik (ed.) et al., Inductive logic programming. 17th
    international conference, ILP 2007, Corvallis, OR, USA, June 19‒21,
    2007. Revised selected papers. Berlin: Springer (ISBN
    978-3-540-78468-5/pbk). Lecture Notes in Computer Science 4894. Lecture
    Notes in Artificial Intelligence, 269-279 (2008).
py: 2008
pu: Berlin: Springer
la: EN
cc: 
ut: ensembles; first order decision trees; comprehensibility
ci: 
li: doi:10.1007/978-3-540-78469-2_26
ab: Summary: Ensemble methods are popular learning methods that are usually
    able to increase the predictive accuracy of a classifier. On the other
    hand, this comes at the cost of interpretability, and insight in the
    decision process of an ensemble is hard to obtain. This is a major
    reason why ensemble methods have not been extensively used in the
    setting of inductive logic programming. In this paper we aim to
    overcome this issue of comprehensibility by learning a single first
    order interpretable model that approximates the first order ensemble.
    The new model is obtained by exploiting the class distributions
    predicted by the ensemble. These are employed to compute heuristics for
    deciding which tests are to be used in the new model. As such we obtain
    a model that is able to give insight in the decision process of the
    ensemble, while being more accurate than the single model directly
    learned on the data.
rv: