Notes on the improvement of concept-cognitive learning accuracy

Abstract

The concept-cognitive learning (CCL) process is the specific implementation step of simulating the human brain to learn concepts, and the CCL model is its core carrier. Different CCL models constructed by different cognitive minds will produce different concept learning results. The existing CCL model based on sufficient and necessary granule approximations regards the human's approximation idea in the face of inconsistent information as a logical criterion, and aims at finding the closest concept pair of the clue as concept learning results with a certain learning accuracy. However, the existing CCL method based on sufficient and necessary granule approximations cannot guarantee that the clue must be between its lower approximation and upper approximation, which causes the fact that the learning accuracy may not effectively measure the consistency of concept learning results. What is more, although the computational process obeys logical cognitive condition, the concept learning results may not conform to the actual situation, such as the case of merely generating full concepts and empty concepts. For the first problem, we improve the learning accuracy of the existing CCL method, propose a new CCL method with learning accuracy under hybrid lattice structure, and develop CCL algorithms for the cases of objects and attributes as clues. Moreover, experiments show the effectiveness of the proposed CCL method with learning accuracy under hybrid lattice structure. For the second problem, we put forward a CCL method based on non-logical associative mechanism to handle the unreasonable situation where the concept learning results are full concepts and empty concepts. Finally, two associative CCL algorithms are explored, and experiments are conducted to show their effectiveness.