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.
In @cite_11 are presented a logic language, SeqLog, for mining sequences of logical atoms, and the inductive mining system MineSeqLog, that combines principles of the level-wise search algorithm with the version space in order to find all patterns that satisfy a constraint by using an optimal refinement operator for SeqLog. SeqLog is a logic representational framework that adopts two operators to represent the sequences: one to indicate that an atom is the direct successor of another and the other to say that an atom occurs somewhere after another. Furthermore, based on this language, the notion of subsumption, entailment and a fix point semantic are given.
@cite_11
1498433609
A logical language, SeqLog, for mining and querying sequential data and databases is presented. In SeqLog, data takes the form of a sequence of logical atoms, background knowledge can be specified using Datalog style clauses and sequential queries or patterns correspond to subsequences of logical atoms. SeqLog is then used as the representation language for the inductive database mining system MineSeqLog. Inductive queries in MineSeqLog take the form of a conjunction of a monotonic and an anti-monotonic constraint on sequential patterns. Given such an inductive query, MineSeqLog computes the borders of the solution space. MineSeqLog uses variants of the famous level-wise algorithm together with ideas from version spaces to realize this. Finally, we report on a number of experiments in the domains of user-modelling that validate the approach.