[Show abstract][Hide abstract] ABSTRACT: Set-valued information systems are generalized models of single-valued information sys-tems. 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 ma-trices 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 experi-ments 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.
International Journal of Approximate Reasoning 06/2012; 53(4):620-635. DOI:10.1016/j.ijar.2012.01.001 · 2.45 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database
is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental
matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object
set varies over time. Experimental results validate the feasibility of the incremental learning optimization.
[Show abstract][Hide abstract] ABSTRACT: Approximations of a concept by a variable precision rough-set model (VPRS) usually vary under a dynamic information system environment. It is thus effective to carry out incremental updating approximations by utilizing previous data structures. This paper focuses on a new incremental method for updating approximations of VPRS while objects in the information system dynamically alter. It discusses properties of information granulation and approximations under the dynamic environment while objects in the universe evolve over time. The variation of an attribute's domain is also considered to perform incremental updating for approximations under VPRS. Finally, an extensive experimental evaluation validates the efficiency of the proposed method for dynamic maintenance of VPRS approximations.
IEEE Transactions on Knowledge and Data Engineering 01/2011; 25(99-PP):1 - 1. DOI:10.1109/TKDE.2011.220 · 2.07 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.