Incremental updating of approximations in composite ordered decision systems under attribute generalization

被引：0
|
作者：
Huang, Qianqian [1 ]
Li, Tianrui [1 ]
Huang, Yanyong [1 ]
Yang, Xin [1 ]
机构：
[1] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Sichuan, Peoples R China
来源：
DATA SCIENCE AND KNOWLEDGE ENGINEERING FOR SENSING DECISION SUPPORT | 2018年 / 11卷
基金：
美国国家科学基金会;
关键词：
Rough sets; decision systems; matrix; incremental learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we first propose a general composite ordered rough set model, which can deal with various types of attributes with a preference order. Then, we present a matrix method for computing approximations in composite ordered decision systems. Finally, we introduce an incremental method for updating approximations in composite ordered decision systems under the variation of attributes.
引用
收藏
页码：453 / 460
页数：8
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