A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values.
ABSTRACT In rough set theory, upper and lower approximations for a concept will change dynamically as the information system changes over time. How to update approximations based on the original information is an important task that can help improve the efficiency of knowledge discovery. This paper focuses on the approach of dynamically updating approximations when attribute values are coarsened or refined. The main contributions include: (1) defining coarsening and refining attribute values in information systems and introducing the properties and the principles of coarsening and refining attribute values; (2) analyzing the properties for dynamic maintenance in terms of upper and lower approximations with coarsening and refining attribute values; (3) proposing an incremental algorithm for updating the approximations of a concept as coarsening or refining attributes values; and finally (4) validating the efficiency of the proposed approach to handle the dynamic maintenance of the approximations for a given concept. © 2010 Wiley Periodicals, Inc.
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ABSTRACT: Many real data sets in databases may vary dynamically. With such data sets, one has to run a knowledge acquisition algorithm repeatedly in order to acquire new knowledge. This is a very time-consuming process. To overcome this deficiency, several approaches have been developed to deal with dynamic databases. They mainly address knowledge updating from three aspects: the expansion of data, the increasing number of attributes and the variation of data values. This paper focuses on attribute reduction for data sets with dynamically varying data values. Information entropy is a common measure of uncertainty and has been widely used to construct attribute reduction algorithms. Based on three representative entropies, this paper develops an attribute reduction algorithm for data sets with dynamically varying data values. When a part of data in a given data set is replaced by some new data, compared with the classic reduction algorithms based on the three entropies, the developed algorithm can find a new reduct in a much shorter time. Experiments on six data sets downloaded from UCI show that the algorithm is effective and efficient.Applied Soft Computing 01/2013; 13(1):676–689. · 2.68 Impact Factor
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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. · 1.98 Impact Factor
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ABSTRACT: Rough set theory is an effective tool to deal with information with uncertainty, and has been successfully applied in many fields. Incremental learning as an efficient strategy for data analysis in dynamic environment enables acquiring additional knowledge from new information by using prior knowledge and has drawn the widespread attentions of many scholars. In this paper, the authors discuss the status of incremental learning researches on rough sets and give potential future research directions. The authors first review basic concepts of rough sets and list three variations of information system in the dynamic decision procedures. Then, the authors investigate and summarize the corresponding incremental learning strategies for the three variations with different research viewpoints, respectively. Finally, the authors further tease out the research framework of their work and identify some future possible research directions.International Journal of Rough Sets and Data Analysis. 07/2014; 1(1):99-112.