A self-learning model based on granular computing
Quan Gan, Guoyin Wang, Jun Hu · 2006
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.