  Sequence classification is the supervised learning task of building models
that predict class labels of unseen sequences of symbols. Although accuracy is
paramount, in certain scenarios interpretability is a must. Unfortunately, such
trade-off is often hard to achieve since we lack human-independent
interpretability metrics. We introduce a novel sequence learning algorithm,
that combines (i) linear classifiers - which are known to strike a good balance
between predictive power and interpretability, and (ii) background knowledge
embeddings. We extend the classic subsequence feature space with groups of
symbols which are generated by background knowledge injected via word or graph
embeddings, and use this new feature space to learn a linear classifier. We
also present a new measure to evaluate the interpretability of a set of
symbolic features based on the symbol embeddings. Experiments on human activity
recognition from wearables and amino acid sequence classification show that our
classification approach preserves predictive power, while delivering more
interpretable models.
