TY - GEN
T1 - definability in logic and rough set theory 1
AU - Fan, Tuan Fang
AU - Liau, Churn Jung
AU - Liu, Duen-Ren
N1 - Publisher Copyright:
© 2008 The authors and IOS Press. All rights reserved.
PY - 2008/6
Y1 - 2008/6
N2 - Rough set theory is an effective tool for data mining. According to the theory, a concept is definable if it can be written as a Boolean combination of equivalence classes induced from classification attributes. On the other hand, definability in logic has been explicated by Beth’s theorem. In this paper, we propose two data representation formalisms, called first-order data logic (FODL) and attribute value-sorted logic (AVSL), respectively. Based on these logics, we explore the relationship between logical definability and rough set definability.
AB - Rough set theory is an effective tool for data mining. According to the theory, a concept is definable if it can be written as a Boolean combination of equivalence classes induced from classification attributes. On the other hand, definability in logic has been explicated by Beth’s theorem. In this paper, we propose two data representation formalisms, called first-order data logic (FODL) and attribute value-sorted logic (AVSL), respectively. Based on these logics, we explore the relationship between logical definability and rough set definability.
UR - http://www.scopus.com/inward/record.url?scp=62449299793&partnerID=8YFLogxK
U2 - 10.3233/978-1-58603-891-5-749
DO - 10.3233/978-1-58603-891-5-749
M3 - Conference contribution
AN - SCOPUS:62449299793
SN - 978158603891
T3 - Frontiers in Artificial Intelligence and Applications
SP - 749
EP - 750
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
T2 - 18th European Conference on Artificial Intelligence, ECAI 2008
Y2 - 21 July 2008 through 25 July 2008
ER -