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.

智能系统研究最多的应用领域之一是分类领域，这是因为分类涵盖了广泛的现实世界问题。一些示例是故障诊断、图像分割、医学诊断等。在大多数情况下，为解决此类问题而设计的智能系统是基于监督学习的，监督学习是基于学习如何与以前的数据集进行分类以找到输入和输出之间的关系。本文的主要重点是使用一种新的数据不确定性建模策略来监督生成通用 2 类模糊分类器。所提出的方法包括混合概念，例如使用嵌入式类型 1 隶属函数、统计概念（例如四分位数）和自然启发的优化方法。