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Multi-scale variable precision covering rough sets and its applications
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AI
DOI：10.1007/s10489-023-05182-3.png)
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Granular computing is required for data mining and knowledge discovery because the complexity and imprecision of data in the real world are too great. Many academics have highlighted is concerned regarding multi-scale information decision tables since single-scale information decision tables can't handle the demands of complicated practical situations. In this paper, we suggest a new data analysis model and integrate the concept of variable accuracy into multi-scale covering rough sets, allowing the model to have some fault-tolerant characteristics and better manage uncertain and imprecise information. By completing this, we are able to create a new type of decision table known as a multi-scale variable precision covering decision table. Both consistent multi-scale variable precision covering decision tables and inconsistent multi-scale variable precision covering decision tables provide techniques for choosing the optimal scale. The three-way decision concept was used to develop a knowledge acquisition rule algorithm that improves the applicability and universality of decision rules.
Keyword:
Multi-scale
Variable precision covering rough set
Optimal scale selection
Knowledge acquisition rule
Three-way decision
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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