Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis

One of the most studied application areas of intelligent systems is the classification area, and this is because classification covers a wide range of real-world problems. Some examples are fault-diagnosis, image segmentation, medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on learning how to classify with previous datasets for finding relations between the inputs and outputs. The main focus of the present paper is the supervised generation of general type-2 fuzzy classifiers with a new strategy for modeling data uncertainty. The proposed methodology includes a mix of concepts, such as the use of embedded type-1 membership functions, statistical concepts such as the quartiles, and nature inspired optimization methods. The classifiers generated with the proposed methodology are compared with respect to other general type-2 fuzzy classifiers based on symmetric uncertainty to evaluate their performance, in this way obtaining interesting results for medical diagnosis with benchmark data sets.

Automated summary

This paper focuses on the application of intelligent systems in the classification area, specifically on generating general type-2 fuzzy classifiers with a new strategy to model data uncertainty. The proposed methodology includes a mix of concepts and optimization methods, showing interesting results on benchmark data sets for medical diagnosis.