Information cells and information cell mixture models for concept modelling

Information cells and information cell mixture models for concept modelling

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Article ID: iaor20123431
Volume: 195
Issue: 1
Start Page Number: 311
End Page Number: 323
Publication Date: May 2012
Journal: Annals of Operations Research
Authors: ,
Keywords: belief functions
Abstract:

By combining the prototype theory and random set theory interpretations of vague concepts, a novel structure named information cell and a combined structure named information cell mixture model are proposed to represent the semantics of vague concepts. An information cell L i on the domain Ω has a transparent cognitive structure ‘L i =about P i ’ which is mathematically formalized by a 3‐tuple ⟨P i ,d i ,δ i ⟩ comprising a prototype set P i (⊆Ω), a distance function d i on Ω and a density function δ i on [0,+∞). An information cell mixture model on domain Ω is actually a set of weighted information cells L i s. A positive neighborhood function of the information cell mixture model is introduced in this paper to reflect the belief distribution of positive neighbors of the underlying concept. An information cellularization algorithm is also proposed to learn the information cell mixture model from a training data set, which is a direct application of the k‐means and EM algorithms. Information cell mixture models provide some tools for information coarsening and concept modelling, and have potential applications in uncertain reasoning and classification.

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