TITLE:
Approximation Maintenance When Adding a Conditional Value in Set-Valued Ordered Decision Systems
AUTHORS:
Xiaoyu Wang, Yuebin Su
KEYWORDS:
Information Systems, Rough Set, Attribute Value, Incremental Method
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.14 No.2,
February
27,
2024
ABSTRACT: The integration of set-valued ordered rough set models and incremental
learning signify a progressive advancement of conventional rough set theory,
with the objective of tackling the heterogeneity and ongoing transformations in
information systems. In set-valued ordered decision systems, when changes occur
in the attribute value domain, such as adding conditional values, it may result
in changes in the preference relation between objects, indirectly leading to
changes in approximations. In this paper, we effectively
addressed the issue of updating approximations that arose from adding conditional values in set-valued
ordered decision systems. Firstly, we classified the research objects into two
categories: objects with changes in conditional values and objects without
changes, and then conducted theoretical studies on updating approximations for
these two categories, presenting approximation update theories for adding
conditional values. Subsequently, we presented incremental algorithms
corresponding to approximation update theories. We demonstrated the feasibility
of the proposed incremental update method with numerical examples and showed
that our incremental algorithm outperformed the static algorithm. Ultimately,
by comparing experimental results on different datasets, it is evident that the
incremental algorithm efficiently reduced processing time. In conclusion, this
study offered a promising strategy to address the challenges of set-valued
ordered decision systems in dynamic environments.