Rule Acquisition Based on Attribute Partial Order Structure Diagram

Attribute Partial Order Structure Diagram (APOSD), as an emergent model of concept cognitive learning, has gathered lots of attention due to its good ability of visual knowledge representation. However, there are few in-depth studies on the theory and method of rule acquisition based on APOSD. This paper explores the issue shown above. Firstly, we propose two types of rules, i.e., loose-type rules and rigorous-type rules. Secondly, we investigate the acquisition methods of the above two types of rules. Extraction of non-redundant loose-type rules is discussed as well. Thirdly, we study the method of using the extracted rules for classification. Finally, we conduct comparison experiments on four chosen UCI data sets with classical machine learning algorithms. Results indicate that the method presented in this article performs well and has the advantages of visual expression of knowledge structure and semantic interpretation.