Title: Rule acquisition based on attribute partial order structure diagram

Authors: Yunli Ren; Huaixin Liang; Jialin Song; Cunfang Zheng; Wenxue Hong

Addresses: School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China; College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, China ' Qinhuangdao Centre for Disease Control and Prevention, Qinhuangdao, Hebei, China ' School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China ' School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China; Liren College, Yanshan University, Qinhuangdao, Hebei, China ' School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China

Abstract: 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.

Keywords: formal concept analysis; rule acquisition; attribute partial order structure diagram; non-redundant rule; decision formal context.

DOI: 10.1504/IJCAT.2022.123476

International Journal of Computer Applications in Technology, 2022 Vol.68 No.2, pp.189 - 202

Received: 12 Nov 2021
Accepted: 02 Feb 2022
Published online: 22 Jun 2022 *

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