A novel approach to concept-cognitive learning in interval-valued formal contexts: a granular computing viewpoint

Concept-cognitive learning (CCL) is to make machines like human beings have the ability of summarizing and reasoning. Automatically learn and find concepts from given information clues is a research focus of CCL. The existing researches mainly focuses on the concept learning methods in classical and fuzzy formal contexts, but there are few researches on the CCL of interval-valued contexts. In view of the universality of interval values in practical applications, we study the mechanism of CCL in interval-valued formal contexts. Firstly, we propose interval-valued formal contexts and a pair of dual cognitive operators as the fundamental foundation of concept learning. Then we mine the relationship between interval-valued information granules and concepts from cognitive learning and granular computing perspective. Then we systematically study the mechanism of interval-valued CCL from the establishment of interval-valued information granules (IvIGs) and its mathematical properties, and the transformation between different information granules (IGs) and clue oriented concept learning. Moreover, three algorithms are established to automatically learn concepts from different clue information. Finally, we download eight public data sets to verify the effectiveness and feasibility of the proposed algorithms from the perspective of the size of extension of concepts, running time of concept learning algorithms and the number of concepts learned by the concept learning algorithms. The experimental comparison indicates that the proposed algorithms are effective and feasible for interval-valued CCL.