An Emerging Incremental Fuzzy Concept-Cognitive Learning Model Based on Granular Computing and Conceptual Knowledge Clustering

Fuzzy granular concepts are fundamental units in developing computational intelligence approaches based on fuzzy concept-cognitive learning. However, existing models in this field merely focus on the information provided by fuzzy granular concepts induced by objects, ignoring that of those induced by attributes. Consequently, these models underutilize the information provided by fuzzy granular concepts and weaken classification ability. To solve this problem, we propose an effective fuzzy granular concept-cognitive learning model, which incorporates fuzzy attribute granular concepts on the basis of the fuzzy object granular concepts. To be concrete, we firstly introduce the notion of a fuzzy attribute granular concept and construct a fuzzy granular concept space. Secondly, we obtain a fuzzy granular concept clustering space by optimizing the threshold which is used to fuse similar fuzzy granular concepts, and then form lower and upper approximation spaces through set approximation. In addition, we explain the mechanism of new incremental fuzzy concept-cognitive learning model for label prediction by integrating the fuzzy granular concept clustering space and the lower and upper approximation spaces. Finally, we show the classification performance of the proposed model on 28 datasets by comparing it with 10 classical machine learning classification algorithms and 17 fuzzy similarity-based classification algorithms, and evaluate incremental learning ability of our model. The experimental results demonstrate the feasibility and effectiveness of our method.