Information cells and information cell mixture models for concept modelling

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.

Hierarchical Concept Learning by Fuzzy Semantic Cells

“…Lawry and Tang introduced an approach to uncertainty modeling for vague concepts by combining the prototype theory and the random set theory [10,11]. In the following in-depth studies, they developed a semantic representation of modeling uncertain concepts called Information Cell [12][13][14]. Tang and Xiao [15] also adopt Fuzzy Semantic Cell (FSC) to name this model.…”