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Probabilistic frequent subtree kernels

: Welke, P.; Horvath, T.; Wrobel, S.


Ceci, M.:
New frontiers in mining complex patterns : 4th international workshop, NFMCP 2015, held in conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015. Revised selected papers
Cham: Springer, 2016 (Lecture Notes in Computer Science 9607)
ISBN: 978-3-319-39314-8 (print)
ISBN: 978-3-319-39315-5
International Workshop on New Frontiers in Mining Complex Patterns (NFMCP) <4, 2015, Porto>
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2015, Porto>
Conference Paper
Fraunhofer IAIS ()

We propose a new probabilistic graph kernel. It is defined by the set of frequent subtrees generated from a small random sample of spanning trees of the transaction graphs. In contrast to the ordinary frequent subgraph kernel it can be computed efficiently for any arbitrary graphs. Due to its probabilistic nature, the embedding function corresponding to our graph kernel is not always correct. Our empirical results on artificial and real-world chemical datasets, however, demonstrate that the graph kernel we propose is much faster than other frequent pattern based graph kernels, with only marginal loss in predictive accuracy.