Self-learning Model of Knowledge Under Uncertain Conditions

Abstract

At present, knowledge learning under uncertain conditions was less efficient. To this end, an autonomous knowledge learning model was established under uncertain conditions. By studying the uncertainty of decision tables and decision rules; establishing a theoretical framework based on rough set representation, measurement and processing of uncertainty information and knowledge; combined with Skowron’s default rule acquisition algorithm, a data autonomous learning models and methods under uncertainty conditions were proposed to solve this problem. The effectiveness of the autonomous learning method was verified by experiments.