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Article type: Research Article
Authors: Wen, Hailia; b | Xia, Feib; * | Tang, Hongxiangb; *
Affiliations: [a] Institute of Intellectual Property, Nanjing University of Science and Technology, Nanjing, Jiangsu, P.R. China | [b] Collaborative Innovation Center for Integration of Terrestrial & Marine Economies, Guangxi University of Finance and Economics, Nanning, Guangxi, P.R. China
Correspondence: [*] Corresponding authors. Fei Xia, Hongxiang Tang, Collaborative Innovation Center for Integration of Terrestrial & Marine Economies, Guangxi University of Finance and Economics, Nanning, Guangxi 530003, P.R. China. E-mails: feixia100@126.com (Fei Xia); hongxiangtang100@126.com (Hongxiang Tang)
Abstract: An information system (IS) is a database that expresses relationships between objects and attributes. An IS with decision attributes is said to be a decision information system (DIS). An incomplete real-valued decision information system (IRVDIS) is a DIS based on incomplete real-valued data. This paper studies three-way decision (3WD) for incomplete real-valued data and its application. In the first place, the distance between two objects on the basis of the conditional attribute set in an IRVDIS is constructed. In the next place, the fuzzy Tcos-equivalence relation on the object set of an IRVDIS is received by means of Gaussian kernel. After that, the decision-theoretic rough set model for an IRVDIS is presented. Furthermore, the 3WD method is proposed based on this model. Lastly, to illustrate the feasibility of the proposed method, an application of the proposed method is given. It is worth mentioning that levels of risk may be determined by thresholds that can be directly acquired according to risk preference of different decision-makers, as well as the decision rule for each decision class under different levels of risk is showed in tabular forms.
Keywords: 3WD, IRVDIS, Method, Decision-theoretic rough set, Gaussian kernel, Inclusion degree, Auto diagnostic
DOI: 10.3233/JIFS-201272
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7843-7862, 2020
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