Abstract
We are interested in learning complex combinatorial features from relational data. We rely on an expressive and general representation language whose semantics allows us to express many features that have been used in different statistical relational learning settings. To avoid expensive exhaustive search over the space of relational features, we introduce a heuristic search algorithm guided by a generalized relational notion of information gain and a discriminant function. The algorithm succesfully finds interesting and interpretable features on artificial and real-world relational learning problems.
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Frasconi, P., Jaeger, M., Passerini, A. (2008). Feature Discovery with Type Extension Trees. In: Železný, F., Lavrač, N. (eds) Inductive Logic Programming. ILP 2008. Lecture Notes in Computer Science(), vol 5194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85928-4_13
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DOI: https://doi.org/10.1007/978-3-540-85928-4_13
Publisher Name: Springer, Berlin, Heidelberg
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