Sequences Classi cation by Least General Generalisations

In this paper, we present a general framework for supervised classi cation. This framework provides methods like boosting and only needs the de nition of a generalisation operator called lgg. For sequence classi cation tasks, lgg is a learner that only uses positive examples. We show that grammatical inference has already de ned such learners for automata classes like reversible automata or k-TSS automata. Then we propose a generalisation algorithm for the class of balls of words. Finally, we show through experiments that our method e ciently resolves sequence classi cation tasks.