Sequences classification by least general generalisations

Authors:
Frédéric Tantini;Alain Terlutte;Fabien Torre
Affiliations:
Parole, CNRS, LORIA Nancy, Université Lille Nord de France;Mostrare, INRIA Lille Nord Europe et CNRS LIFL, Université Lille Nord de France;Mostrare, INRIA Lille Nord Europe et CNRS LIFL, Université Lille Nord de France
Venue:
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Year:
2010

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Abstract

In this paper, we present a general framework for supervised classification. This framework provides methods like boosting and only needs the definition of a generalisation operator called LGG. For sequence classification tasks, LGG is a learner that only uses positive examples. We show that grammatical inference has already defined 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 efficiently resolves sequence classification tasks.