Enhanced type-2 Wang-Mendel Approach
Prashant Kumar Gupta, Javier Andreu-Pérez · Journal of Experimental & Theoretical Artificial Intelligence · 2022
The Wang-Mendel Approach (WMA) focuses on combining the numerical as well as linguistic information for achieving greater explainability for inference models. The standard WMA models the linguistic information using type-1 (T1) fuzzy sets (FSs), which have a reduced capability to model the semantics of linguistic information. Therefore, we propose a novel Enhanced WMA, which models the linguistic information using the type-2 (T2) FSs. Further, our Enhanced T2 FS based WMA can be modified to reflect the use of interval type-2 (IT2) FSs, for modeling linguistic uncertainty. IT2 FSs are suitable when better uncertainty handling capabilities are required compared to T1 FSs, however, at a computational cost lesser than the T2 FSs. Performance of Enhanced WMA is demonstrated through a real-world crop-yield prediction problem in smart agriculture and an additional exemplar application on users' satisfaction ratings. Further, we have compared our approach with the performance obtained from the T1 FS based WMA and the original estimations given in the original data. We found that our Enhanced WMA achieves better precision than the other two with 95% confidence level. To the best of our knowledge, no one has proposed the use of T2 FSs for modeling linguistic uncertainty in the WMA before.