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Concept Learning from (Very) Ambiguous Examples

Abstract : We investigate here concept learning from incomplete examples, denoted here as ambiguous. We start from the learning from interpretations setting introduced by L. De Raedt and then follow the informal ideas presented by H. Hirsh to extend the Version space paradigm to incomplete data: a hypothesis has to be compatible with all pieces of information provided regarding the examples. We propose and experiment an algorithm that given a set of ambiguous examples, learn a concept as an existential monotone DNF, We show that 1) boolean concepts can be learned, even with very high incompleteness level as long as enough information is provided, and 2) monotone, non monotone DNF (i.e. including negative literals), and attribute-value hypotheses can be learned that way, using an appropriate background knowledge. We also show that a clever implementation, based on a multi-table representation is necessary to apply the method with high levels of incompleteness.
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https://hal.inria.fr/inria-00472599
Contributor : Veronique Ventos <>
Submitted on : Monday, April 12, 2010 - 3:47:09 PM
Last modification on : Thursday, July 8, 2021 - 3:47:58 AM

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Veronique Ventos, Dominique Bouhtinon, Henry Soldano. Concept Learning from (Very) Ambiguous Examples. Machine Learning and Data Mining MLDM 2009, Jul 2009, Leipzig, Germany. pp.465-478, ⟨10.1007/978-3-642-03070-3_35⟩. ⟨inria-00472599⟩

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