An incremental learning mechanism for object classification based on progressive fuzzy three-way concept

Abstract

Fuzzy concept has been an important methodology for data analysis, especially in the classification research. Particularly, fuzzy concept could directly process the continuous data through contrasting the numerical data into the membership degree of object to attribute. However, the classical fuzzy concept only focuses on the positive information, that is, the information about membership degree, while ignoring non-membership degree. Meanwhile, since the limitations of individual cognition and cognitive environment, the concept learning is progressive. Inspired by these thoughts, we design an incremental learning mechanism based on progressive fuzzy three-way concept for object classification in dynamic environment. In this paper, the object and attribute learning operators are first defined to obtain fuzzy three-way concept. Then, a progressive fuzzy three-way concept and its corresponding concept space are learned considering the progressive process of concept learning. Moreover, the object classify mechanism and dynamic update mechanism based on the progressive concept space are proposed, and their effectiveness is verified by numerical experiments. Finally, an incremental learning mechanism is further designed for dynamic increased data and compared with other fuzzy classify methods. All the experimental results carried on ten datasets from UCI and KEEL illustrate the proposed learning mechanism is an excellent object classify algorithm.