Journal
ARTIFICIAL INTELLIGENCE
Volume 174, Issue 7-8, Pages 479-499Publisher
ELSEVIER
DOI: 10.1016/j.artint.2010.02.002
Keywords
Dempster-Shafer theory; Evidence theory; Conjunctive knowledge; Lattice; Uncertain reasoning; Multi-label classification
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A formalism is proposed for representing uncertain information on set-valued variables using the formalism of belief functions. A set-valued variable X on a domain Omega is a variable taking zero, one or several values in Omega. While defining mass functions on the frame 2(2 Omega) is usually not feasible because of the double-exponential complexity involved, we propose an approach based on a definition of a restricted family of subsets of 2(Omega) that is closed under intersection and has a lattice structure. Using recent results about belief functions on lattices, we show that most notions from Dempster-Shafer theory can be transposed to that particular lattice, making it possible to express rich knowledge about X with only limited additional complexity as compared to the single-valued case. An application to multi-label classification (in which each learning instance can belong to several classes simultaneously) is demonstrated. (C) 2010 Elsevier B.V. All rights reserved.
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