A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The knowledge characteristics weighting plays an extremely important role in effectively and accurately classifying knowledge.
Most of the existing characteristics weighting methods always rely heavily on the experts’ a priori knowledge, while rough set weighting method does not rely on experts’ a priori knowledge and can meet the need of objectivity.
However, the current rough set weighting methods could not obtain a balanced redundant characteristic set.
Too much redundancy might cause inaccuracy, and less redundancy might cause ineffectiveness.
In this paper, a new method based on rough set and knowledge granulation theories is proposed to ascertain the characteristics weight.
Experimental results on several UCI data sets demonstrate that the weighting method can effectively avoid subjective arbitrariness and avoid taking the nonredundant characteristics as redundant characteristics.
American Psychological Association (APA)
Shi, Zhenquan& Chen, Shiping. 2018. A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130607
Modern Language Association (MLA)
Shi, Zhenquan& Chen, Shiping. A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1130607
American Medical Association (AMA)
Shi, Zhenquan& Chen, Shiping. A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130607
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130607