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Using multi-granular fuzzy linguistic modelling methods for supervised classification learning purposes
(2017 IEEE International conference on fuzzy systems (FUZZ-IEEE), 2017)
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 ...
Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy
(IEEE Transactions on Fuzzy Systems, 2017-10)
Obtaining good classification results using supervised learning methods is critical if we want to obtain a high level of precision in the classification processes. The training data used for the learning process plays a ...
Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods
(Knowledge-Based Systems, 2017-12)
Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, ...