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Sequences Classification by Least General Generalisations

Frédéric Tantini 1, 2 Alain Terlutte 3 Fabien Torre 3, 4
2 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
4 MOSTRARE - Modeling Tree Structures, Machine Learning, and Information Extraction
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
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 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.
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Submitted on : Friday, October 8, 2010 - 3:55:26 PM
Last modification on : Friday, February 26, 2021 - 3:28:06 PM
Long-term archiving on: : Thursday, October 25, 2012 - 4:45:17 PM


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Frédéric Tantini, Alain Terlutte, Fabien Torre. Sequences Classification by Least General Generalisations. 10th International Colloquium on Grammatical Inference, Sep 2010, Valencia, Spain. pp.189-202, ⟨10.1007/978-3-642-15488-1_16⟩. ⟨inria-00524707⟩



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