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.
Finally, in @cite_2 an extension of conditional random fields (CRFs) to logical sequences has been proposed. In the case of sequence labelling task, CRFs are a better alternative to HMMs that makes it relatively easy to model arbitrary dependencies in the input space. CRFs are undirected graphical models that instead of learning a generative model, such as in HMMs, they learn a discriminative model designed to handle non-independent input features. @cite_2 , the authors lifted CRFs to the relational case by representing the potential functions as a sum of relational regression trees learnt by a relational regression tree learner.
@cite_2
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Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat alphabets. In this paper, we describe TildeCRF, the first method for training CRFs on logical sequences, i.e., sequences over an alphabet of logical atoms. TildeCRF's key idea is to use relational regression trees in 's gradient tree boosting approach. Thus, the CRF potential functions are represented as weighted sums of relational regression trees. Experiments show a significant improvement over established results achieved with hidden Markov models and Fisher kernels for logical sequences.