Incremental fuzzy probabilistic rough sets over two universes

被引：0

作者：

Hu, Jie ^{[1
]}

Li, Tianrui ^{[1
]}

Luo, Chuan ^{[2
]}

Fujita, Hamido ^{[3
]}

Li, Shaoyong ^{[4
]}

机构：

[1] School of Information Science and Technology, Southwest Jiaotong University, Chengdu,611756, China

[2] College of Computer Science, Sichuan University, Chengdu,610065, China

[3] Fac. of Software and Information Science, Iwate Prefectural University, Iwate,020-0693, Japan

[4] Naval Aviation Institute, PLA, Huludao,125001, China

来源：

International Journal of Approximate Reasoning | 2017年 / 81卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Rough set theory - Diagnosis;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The fuzzy Information System over Two Universes (ISTU) formalizing a data table corresponding to two universes as well as their relations is common in real-world applications, e.g., recommender system and clinical diagnosis system. The fuzzy probabilistic rough sets over two universes (FPRSMTU) can deal with a fuzzy relation and allow a tolerance inaccuracy in the construction of rough approximations in a fuzzy ISTU, which is a generalization of classic rough sets with fuzzy and probabilistic theories. As a necessary step for knowledge discovery based on rough sets, the fuzzy rough approximations of fuzzy ISTU need to be updated efficiently under dynamic data environment. Incremental technique is an efficient approach for dynamic information processing by making full use of previously obtained knowledge. In this paper, incremental approaches for updating approximations of fuzzy ISTU are proposed while some objects adding into or deleting from the two universes, and the corresponding incremental algorithms are designed. Experimental evaluations on real datasets as well as artificial datasets show the effectiveness of the proposed incremental updating method compared with the non-incremental method. © 2016 Elsevier Inc.

引用

页码：28 / 48