A Variable Precision Rough Set Model for Knowledge-assisted Management in Distance Education

Authors

  • Wei Zhang 1Wuhan University of Technology, Wuhan, China 2Hubei Engineering University, Xiaogan, China

DOI:

https://doi.org/10.3991/ijet.v13i11.9602

Keywords:

variable precision rough set, knowledge extraction, distance education, knowledge assisted management

Abstract


To enable the teaching administrator to better obtain effective knowledge from a large amount of information to assist management and improve the efficiency and level of teaching management, a variable precision rough set model for knowledge assisted management of distance education was proposed. First, based on the theory of complete reduction and knowledge extraction, the proposed pedigree ambiguity tree was used as a strategy for obtaining complete reduction. An algorithm for obtaining a complete set of reductions was given. Then, by studying the process of knowledge extraction, a multi-knowledge extraction framework was put forward. The process of data conversion was completely realized. Finally, experimental verification was performed. The results showed that the proposed model overcame the effect of noise data in real data and improved the efficiency of the algorithm. Therefore, the model has high universality.

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Published

2018-11-09

How to Cite

Zhang, W. (2018). A Variable Precision Rough Set Model for Knowledge-assisted Management in Distance Education. International Journal of Emerging Technologies in Learning (iJET), 13(11), pp. 41–53. https://doi.org/10.3991/ijet.v13i11.9602

Issue

Vol. 13 No. 11 (2018)

Section

Papers

License

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