A4 Conference proceedings

Using multi-granular fuzzy linguistic modelling methods for supervised classification learning purposes

Publication Details
Authors: Morente-Molinera J. A., Mezei J., Carlsson C., Herrera-Viedma E.
Publication year: 2017
Language: English
Related Journal or Series Information: IEEE International Conference on Fuzzy Systems
Title of parent publication: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Journal acronym: FUZZ-IEEE
ISBN: 978-1-5090-6035-1
eISBN: 978-1-5090-6034-4
ISSN: 1544-5615
eISSN: 1558-4739
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


Classification learning is a very complex process whose success and failure ratio depends on a high amount of elements. One of them is the representation mean used for the data that is employed in the process. Granularity of the data used for classification learning purposes can affect dramatically the success and failure ratio of the obtained classification. In this paper, multi-granular fuzzy linguistic modelling methods are applied over the classification learning data in order to modify their granularity and increase the classification success ratio. Thanks to multi-granular fuzzy linguistic modelling methods, it is possible to automatically modify the data granularity in order to determine which data representation is the one that provides the better classification results in the learning process

Last updated on 2018-19-10 at 07:55