计算机科学 ›› 2012, Vol. 39 ›› Issue (7): 229-231.

• 人工智能 • 上一篇    下一篇

基于覆盖的粗糙集模型比较

王丽娟,杨习贝,杨静宇,吴陈   

  1. (南京理工大学计算机科学与技术学院 南京 210094) (江苏科技大学计算机科学与工程学院 镇江 212003)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Comparison on Covering-based Rough Set Models

  • Online:2018-11-16 Published:2018-11-16

摘要: 通过分别比较各模型的上近似、下近似以及近似精度,系统地分析了6种基于覆盖的粗糙集模型。得到3条结论:首先,在6种覆盖上近似之间,第2种覆盖上近似最大,并且除了第3种和第4种覆盖上近似之间是不可比较的之外,前5种覆盖上近似集间均有包含关系;其次,两种覆盖下近似间存在包含关系;第三,在6种模型的近似精度之间,第2种模型是最低的,而第5种模型除了和第6种模型不可比较之外其具有最高的近似精度。通过多个实例,验证了所有结论的正确性。这种对不同粗糙集模型的对比研究为深入理解这些模型提供了帮助,并且为不同应用场合模型的选择提供了参考依据。

关键词: 覆盖,粗糙集模型,近似精度,模型比较

Abstract: By means of comparing the upper approximation sets, the lower approximation sets and accuracy measures of six covering-based rough set models, the six models were systematically studied. Three conclusions were obtained. First1y, among six covering upper approximation sets, the second one is the largest, and the first five upper approximation sets all have inclusion relationship, except the third and fourth ones. Secondly, there is inclusion relationship between two lower approximation sets. Thirdly, among the accuracy measures of six covering-based rough set models, the second one has the lowest accuracy measure, and the fifth one has the highest accuracy measure, but it has no relationship with the sixth one. I3y some illustrative examples, all the conclusions gain demonstrations. The comparative study on different rough set models provides a better understanding of these models and some references for model selection in different applications.

Key words: Covering, Rough set models, Accuracy measure, Model comparison

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