  We tackle the problem of multi-class relational sequence learning using
relevant patterns discovered from a set of labelled sequences. To deal with
this problem, firstly each relational sequence is mapped into a feature vector
using the result of a feature construction method. Since, the efficacy of
sequence learning algorithms strongly depends on the features used to represent
the sequences, the second step is to find an optimal subset of the constructed
features leading to high classification accuracy. This feature selection task
has been solved adopting a wrapper approach that uses a stochastic local search
algorithm embedding a naive Bayes classifier. The performance of the proposed
method applied to a real-world dataset shows an improvement when compared to
other established methods, such as hidden Markov models, Fisher kernels and
conditional random fields for relational sequences.

Artificial Intelligence;Machine Learning