In the field of machine learning, many methods based on granular computing have been proved as efficient ways for problem solving. In this paper, we proposed a method to divide decision tables into granules at different hierarchies. Using this method, a self-learning algorithm is developed for uncertain information processing. Simulation results proved that this model is valid and efficient.
['Quan Gan', 'Guoyin Wang', 'Jun Hu']
A self-learning model based on granular computing