[1]SUN Haixia.Dynamic updating algorithm of neighborhood decision-theoretic rough set model based on object change[J].CAAI Transactions on Intelligent Systems,2021,16(4):746-756.[doi:10.11992/tis.202010028]
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Dynamic updating algorithm of neighborhood decision-theoretic rough set model based on object change
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 4
Page number:
746-756
Column:
学术论文—知识工程
Public date:
2021-07-05
- Title:
- Dynamic updating algorithm of neighborhood decision-theoretic rough set model based on object change
- Keywords:
- rough set; decision-theoretic rough set; neighborhood; incremental learning; approximation set; object; iteration; dynamic
- CLC:
- TP18
- DOI:
- 10.11992/tis.202010028
- Abstract:
- In accordance with the dynamic characteristics of a dataset in a real environment, an incremental updating algorithm of the neighborhood decision-theoretic rough set model is proposed after conducting simple to complex research. In this paper, the change law of the probability between the target approximation set and the neighborhood class is first analyzed in the context of the domain of the neighborhood information system increasing or decreasing individual objects, which is then adopted to construct the incremental updating of the upper and lower approximation sets of the neighborhood decision-theoretic rough set model. On the basis of the change of a single object and given the variety of multiple objects, an incremental updating algorithm is designed through step-by-step iteration. Experimental results show that the proposed algorithm has a high incremental updating performance, thereby making it suitable for the dynamic updating of the neighborhood decision-theoretic rough set model in a dynamic data environment.
- References:
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1900-01-01