Temporal Probabilistic Concepts from Heterogeneous Data Sequences

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Abstract

We consider the problem of characterisation of sequences of heterogeneous symbolic data that arise from a common underlying temporal pattern. The data, which are subject to imprecision and uncertainty, are heterogeneous with respect to classification schemes, where the class values differ between sequences. However, because the sequences relate to the same underlying concept, the mappings between values, which are not known ab initio, may be learned. Such mappings relate local ontologies, in the form of classification schemes, to a global ontology (the underlying pattern). On the basis of these mappings we use maximum likelihood techniques to handle uncertainty in the data and learn local probabilistic concepts represented by individual temporal instances of the sequences. These local concepts are then combined, thus enabling us to learn the overall temporal probabilistic concept that describes the underlying pattern. Such an approach provides an intuitive way of describing the temporal pattern while allowing us to take account of inherent uncertainty using probabilistic semantics.
Original languageEnglish
Title of host publicationUnknown Host Publication
EditorsD Bustard, W Liu, R Sterritt
Place of PublicationBerlin
PublisherSpringer
Pages191-205
Number of pages15
ISBN (Print)3-540-43481-X
DOIs
Publication statusPublished - Apr 2002
EventSoft-ware 2002: Computing in an Imperfect World - Belfast, Northern Ireland
Duration: 1 Apr 2002 → …

Conference

ConferenceSoft-ware 2002: Computing in an Imperfect World
Period1/04/02 → …

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  • Cite this

    McClean, SI., Scotney, BW., & Palmer, FL. (2002). Temporal Probabilistic Concepts from Heterogeneous Data Sequences. In D. Bustard, W. Liu, & R. Sterritt (Eds.), Unknown Host Publication (pp. 191-205). Springer. https://doi.org/10.1007/3-540-46019-5_15