Methods of qualitative analysis such as qualitative classification have gained importance as an essential complement of existing quantitative analysis in numerous fields, such as behavior finance, econometrics, and business management. Only a few models have been developed to deal with qualitative inputs (attributes), which appear in the form of T2F data. Additionally, classification models are unsuitable if an output point is not fully assigned to a single class. In this paper, we formulate a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM in brief) combined with Type-2 fuzzy expected regression (FER in brief) to deal with T2F inputs. This classifier(FER-FSVM in brief) makes it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, FER-FSVM can self-learn the data structure and shifted between FER or FSVM for classification automatically. It will largely shorten the computing time especially for large datasets by using linear structure of FER classifier to limit the size of non-linear classification region.