Concept Learning from (Very) Ambiguous Examples

Authors:
Dominique Bouthinon;Henry Soldano;Véronique Ventos
Affiliations:
L.I.P.N, UMR-CNRS 7030, Université Paris-Nord, Villetaneuse, France 93430;L.I.P.N, UMR-CNRS 7030, Université Paris-Nord, Villetaneuse, France 93430;LRI, UMR-CNRS 8623, Université Paris-Sud, Orsay, France 91405
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

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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.