Classification and query evaluation using modelling with words

A random set based knowledge representation framework for learning linguistic models is presented. Within this framework a number of algorithms for learning prototypes are proposed, based on grouping certain sets of attributes and evaluating joint mass assignments on labels. These mass assignments c...

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Bibliographic Details
Published in:Information sciences 2006-02, Vol.176 (4), p.438-464
Main Authors: Randon, N.J., Lawry, J.
Format: Article
Language:English
Subjects:
Label prototypes
Label semantics
Mass assignments
Random sets
Semi-Naïve Bayes
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Description
Summary:A random set based knowledge representation framework for learning linguistic models is presented. Within this framework a number of algorithms for learning prototypes are proposed, based on grouping certain sets of attributes and evaluating joint mass assignments on labels. These mass assignments can then be combined with a Semi-Naïve Bayes classifier in order to determine classification probabilities. The potential of such linguistic classifiers is then illustrated by their application to a number of toy and benchmark problems. This framework also allows for the evaluation of linguistic queries as will be demonstrated on several well known data sets.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2005.07.019