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Construction of three-way attribute partial order structure via cognitive science and granular computing
Knowledge-Based Systems ( IF 5.101 ) Pub Date : 2020-04-04 , DOI: 10.1016/j.knosys.2020.105859
Enliang Yan; Jialin Song; Yunli Ren; Cunfang Zheng; Baohong Mi; Wenxue Hong

Partial order formal structure analysis (POFSA), as an emerging model of concept cognitive learning, has been extensively used in the field of knowledge processing. However, along with the development of information storage and network technology, the knowledge that people can master is growing dramatically, and it is difficult to effectively process the expanding knowledge just through one single theory. Therefore, this paper explores the construction method of a three-way attribute partial order structure (APOS) via multi granularity by incorporating the ideas of three-way decision (3WD) and granular computing (GrC) into the theory of POFSA. First, for a specific formal context, by taking object set as the whole domain and using attributes and their respective extensions to constitute granule, granular layers can be formed based on the binary relations of equivalence or compatibility between granules. Then, according to the partial order relations between the granules of different granular layers, the corresponding granular structure APOS can be generated. Finally, using the idea of 3WD to reasonably eliminate the cross connections between different branches of APOS, a three-way APOS via multi granularity can be constructed. In addition, based on the generation algorithm of the three-way APOS, the knowledge processing of several data sets from UCI have been conducted and discussed. Through discussion and experiment, it can be concluded that, the three-way APOS via multi granularity can not only improve the efficiency of knowledge processing, but also make the results of knowledge processing more reasonable.
更新日期:2020-04-06

 

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