An Approach for Incremental Updating Approximations in Variable Precision Rough Sets while Attribute Generalized
Rough set theory (RST) for knowledge updating have been successfully applied in data mining and its correlative domains. As a special type of probabilistic rough set model, Variable precision rough sets (VPRS) model is an extension of RST. For an information system, the VPRS model allows a flexible approximation boundary region by using a precision variable and has a better tolerance ability for inconsistent data. However, the approximations of a concept may change when an information system varies. The approach for incremental updating of approximations while attribute generalizing in VPRS should be considered. In this paper, an incremental model and its algorithm for updating approximations of a concept based on VPRS are proposed when attribute generalized. Examples are employed to validate the feasibility of this approach.
Junbo Zhang Tianrui Li Dun Liu
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China
国际会议
The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)
杭州
英文
77-81
2010-11-15（万方平台首次上网日期，不代表论文的发表时间）