학술논문

Two kinds of average approximation accuracy
Document Type
article
Source
CAAI Transactions on Intelligence Technology, Vol 9, Iss 2, Pp 481-490 (2024)
Subject
rough sets
rough set theory
Computational linguistics. Natural language processing
P98-98.5
Computer software
QA76.75-76.765
Language
English
ISSN
2468-2322
Abstract
Abstract Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is the absolute average approximation accuracy, which is based on undefinable sets and has yielded significant conclusions. We also explore the relationship between these two types of average approximation accuracy. Finally, the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.