Fuzzy concept cognitive learning based on knowledge space theory

Concept cognitive learning (CCL) constitutes a rigorous and current cognitive theory for the representation and learning of concepts of the human brain. The well-established CCL models pay close attention to construct a concept space, in which all the attributes are mastered concurrently. However, few attempts have been made to combine CCL with cognitive logic in a fuzzy context due to attribute precedence. For this case, this paper first develops a cognitive surmise relationship among attributes, cognitive transitions, and discrimination of fuzzy concepts based on knowledge space theory. Furthermore, utilizing the inherent information of concepts, the attributes are weighted to accurately understand and apply fuzzy concepts. To better derive benefits from the fuzziness and uncertainty of knowledge, an approach is provided to improve performance through the fusion of fuzzy concepts. Empirical studies on twenty datasets reveal the effectiveness and efficiency of the proposed model.