Information cells and information cell mixture models for concept modelling

Yongchuan Tang, Jonathan Lawry

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

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 Li on the domain Ω has a transparent cognitive structure ‘Li = about Pi ’ which is mathematically formalized by a 3-tuple Pi, di, δi comprising a prototype set Pi (⊆ Ω), a distance function di on Ω and a density function δi on [0,+∞). An information cell mixture model on domain Ω is actually a set of weighted information cells Li 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.
Original languageEnglish
Pages (from-to)311–323
JournalAnnals of Operations Research
Publication statusPublished - 2012

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