Abstract
This paper tackles the problem of relational sequence learning selecting relevant features elicited from a set of labelled sequences. Each relational sequence is firstly mapped into a feature vector using the result of a feature construction method. The second step finds an optimal subset of the constructed features that leads to high classification accuracy, by adopting a wrapper approach that uses a stochastic local search algorithm embedding a Bayes classifier. The performance of the proposed method on a real-world dataset shows an improvement compared to other sequential statistical relational methods, such as Logical Hidden Markov Models and relational Conditional Random Fields.
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Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F. (2011). Optimizing Probabilistic Models for Relational Sequence Learning. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_27
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DOI: https://doi.org/10.1007/978-3-642-21916-0_27
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