LazyBum: Decision tree learning using lazy propositionalization
This work addresses inefficiencies in propositionalization for relational data learning, offering a more efficient method for researchers and practitioners in machine learning, though it is incremental as it builds on the OneBM approach.
The paper tackles the problem of propositionalization in relational data, where traditional methods may lose information or create unnecessary features, by introducing LazyBum, a system that interleaves feature construction with decision tree learning to guide the process dynamically. It achieves comparable accuracy with lower execution time compared to existing methods like OneBM, nFOIL, and MODL on most datasets.
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.