TY - JOUR
T1 - Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems
AU - Zhang, Junbo
AU - Li, Tianrui
AU - Ruan, Da
AU - Liu, Dun
PY - 2012/6
Y1 - 2012/6
N2 - Set-valued information systems are generalized models of single-valued information systems. The attribute set in the set-valued information system may evolve over time when new information arrives. Approximations of a concept by rough set theory need updating for knowledge discovery or other related tasks. Based on a matrix representation of rough set approximations, a basic vector H(X) is induced from the relation matrix. Four cut matrices of H(X), denoted by H [μ,ν](X), H (μ,ν](X), H [μ,ν)(X) and H (μ,ν)(X), are derived for the approximations, positive, boundary and negative regions intuitively. The variation of the relation matrix is discussed while the system varies over time. The incremental approaches for updating the relation matrix are proposed to update rough set approximations. The algorithms corresponding to the incremental approaches are presented. Extensive experiments on different data sets from UCI and user-defined data sets show that the proposed incremental approaches effectively reduce the computational time in comparison with the non-incremental approach.
AB - Set-valued information systems are generalized models of single-valued information systems. The attribute set in the set-valued information system may evolve over time when new information arrives. Approximations of a concept by rough set theory need updating for knowledge discovery or other related tasks. Based on a matrix representation of rough set approximations, a basic vector H(X) is induced from the relation matrix. Four cut matrices of H(X), denoted by H [μ,ν](X), H (μ,ν](X), H [μ,ν)(X) and H (μ,ν)(X), are derived for the approximations, positive, boundary and negative regions intuitively. The variation of the relation matrix is discussed while the system varies over time. The incremental approaches for updating the relation matrix are proposed to update rough set approximations. The algorithms corresponding to the incremental approaches are presented. Extensive experiments on different data sets from UCI and user-defined data sets show that the proposed incremental approaches effectively reduce the computational time in comparison with the non-incremental approach.
KW - Knowledge discovery
KW - Matrix
KW - Rough sets
KW - Set-valued information systems
UR - http://www.scopus.com/inward/record.url?scp=84862809634&partnerID=8YFLogxK
U2 - 10.1016/j.ijar.2012.01.001
DO - 10.1016/j.ijar.2012.01.001
M3 - Article
AN - SCOPUS:84862809634
SN - 0888-613X
VL - 53
SP - 620
EP - 635
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
IS - 4
ER -