Using multi-granular fuzzy linguistic modelling methods for supervised classification learning purposes
- Resource Type
- Conference
- Authors
- Morente-Molinera, J. A.; Mezei, J.; Carlsson, C.; Herrera-Viedma, E.
- Source
- 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on. :1-6 Jul, 2017
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Pragmatics
Learning systems
Data models
Computational modeling
Classification algorithms
Support vector machines
Electronic mail
- Language
- ISSN
- 1558-4739
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.