Sequences Classification by Least General Generalisations

Frédéric Tantini; Alain Terlutte; Fabien Torre

inria-00524707, version 1

Sequences Classification by Least General Generalisations

Frédéric Tantini () ^1, 2, Alain Terlutte ()^a^, 3, Fabien Torre () ^3, 4

10th International Colloquium on Grammatical Inference 6339 (2010) 189-202

Résumé : 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 ork-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.

a – Université Charles de Gaulle - Lille III
1 : LAboratoire Hubert Curien (LAHC)
CNRS : UMR5516 – Université Jean Monnet - Saint-Etienne
2 : PAROLE (INRIA Lorraine - LORIA)
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
3 : Laboratoire d'Informatique Fondamentale de Lille (LIFL)
CNRS : UMR8022 – Université Lille I - Sciences et technologies – Université Lille III - Sciences humaines et sociales – INRIA
4 : MOSTRARE (INRIA Lille - Nord Europe)
INRIA – CNRS : UMR8022 – Université Lille I - Sciences et technologies – Université Lille III - Sciences humaines et sociales

inria-00524707, version 1
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Contributeur : Fabien Torre <>
Soumis le : Vendredi 8 Octobre 2010, 15:55:26
Dernière modification le : Lundi 18 Octobre 2010, 12:05:11

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DOI : 10.1007/978-3-642-15488-1_16

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