高斯核模糊粗糙集中对象集变化时近似集增量更新方法研究

计算机科学 ›› 2013, Vol. 40 ›› Issue (7): 173-177.

高斯核模糊粗糙集中对象集变化时近似集增量更新方法研究

曾安平,李天瑞,罗川

西南交通大学信息科学与技术学院成都610031;宜宾学院计算机与信息工程学院宜宾644007;西南交通大学信息科学与技术学院成都610031;西南交通大学信息科学与技术学院成都610031

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金项目(61175047,U1230117),四川省教育厅青年基金项目(13ZB0210),西南交通大学博士生创新基金项目(2013)资助

Incremental Approach for Updating Approximations of Gaussian Kernelized Fuzzy Rough Sets under Variation of Object Set

ZENG An-ping,LI Tian-rui and LUO Chuan

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

摘要/Abstract

摘要： 在实际应用中,信息系统中数据的类型是多样的,它可能由类别型、数值型、模糊型等多种形式的数据组成。模糊粗糙集模型可以有效地解决多种类型数据共存情形下的信息处理问题。利用高斯核函数在数值和模糊数据非线性划分上的优势产生模糊关系可以较好地进行模糊粗糙数据分析。而实际的信息系统都是动态变化的,如何利用已有知识来增量更新模糊粗糙集模型的近似集问题是其应用于大数据处理的关键。针对该问题,讨论了模糊信息系统中对象集动态变化时近似集的更新原理,并提出了基于高斯核模糊粗糙集模型的近似集增量更新方法,最后通过实例验证了该方法的正确性和有效性。

关键词: 模糊粗糙集,增量更新,高斯核函数中图法分类号TP391文献标识码A

Abstract: In real-applications,there are many kinds of data in information systems．The data may consist of categorical,numerical,fuzzy values．Fuzzy rough set model can deal with this complex data．Gaussian kernels have been introduced to acquire fuzzy relations between samples described by fuzzy or numeric attributes to carry out fuzzy rough data analysis．In addition,the information systems often vary with time．How to use the previous knowledge to update approximations in fuzzy rough set model is a key step of its application on big data．This paper discussed the principles of updating approximations in fuzzy information systems under the variation of the object set．An approach for incrementally updating approximations of fuzzy rough set was then presented．Some examples were employed to illustrate the proposed approach.

Key words: Fuzzy rough set,Incremental updating,Gaussian kernel

曾安平,李天瑞,罗川. 高斯核模糊粗糙集中对象集变化时近似集增量更新方法研究[J]. 计算机科学, 2013, 40(7): 173-177. https://doi.org/

ZENG An-ping,LI Tian-rui and LUO Chuan. Incremental Approach for Updating Approximations of Gaussian Kernelized Fuzzy Rough Sets under Variation of Object Set[J]. Computer Science, 2013, 40(7): 173-177. https://doi.org/

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