Affiliations: [a] Key Laboratory of Complex System Optimization and Big Data Processing in Department of GuangxiEducation, Yulin Normal University, Yulin, Guangxi, P.R. China | [b] School of Mathematics and Physics, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China | [c] School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China | [d] School of Mathematics and Statistics, Yulin Normal University, Yulin, Guangxi, P.R. China
Correspondence:
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Corresponding author. Liangdong Qu, School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, Guangxi 530006, P.R. China. E-mail: [email protected].
Abstract: Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, θ-reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction algorithms base on θ-discernibility matrix, θ-information entropy and θ-significance in an IIVIS are given.
