  Propositionalization is the process of summarizing relational data into a
tabular (attribute-value) format. The resulting table can next be used by any
propositional learner. This approach makes it possible to apply a wide variety
of learning methods to relational data. However, the transformation from
relational to propositional format is generally not lossless: different
relational structures may be mapped onto the same feature vector. At the same
time, features may be introduced that are not needed for the learning task at
hand. In general, it is hard to define a feature space that contains all and
only those features that are needed for the learning task. This paper presents
LazyBum, a system that can be considered a lazy version of the recently
proposed OneBM method for propositionalization. LazyBum interleaves OneBM's
feature construction method with a decision tree learner. This learner both
uses and guides the propositionalization process. It indicates when and where
to look for new features. This approach is similar to what has elsewhere been
called dynamic propositionalization. In an experimental comparison with the
original OneBM and with two other recently proposed propositionalization
methods (nFOIL and MODL, which respectively perform dynamic and static
propositionalization), LazyBum achieves a comparable accuracy with a lower
execution time on most of the datasets.
