Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis. (30th November 2021) - Ontiveros-Robles, Emanuel; Castillo, Oscar; Melin, Patricia
Highlights: Supervised generation of Type-2 Fuzzy Classifiers with a new strategy for modeling data uncertainty is proposed. The methodology combines embedded type-1 membership functions, statistical concepts, and particle swarm optimization. Classifiers generated with the methodology are compared with Type-2 Fuzzy Classifiers based on symmetric uncertainty. The performance of the proposed approach is measured with medical diagnosis benchmark data sets with good results. Abstract: 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.