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Ma Z, Mi J, Lin Y, Li J. Boundary region-based variable precision covering rough set models. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Wang P, Qu L, Zhang Q. Information entropy based attribute reduction for incomplete heterogeneous data. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Attribute reduction in an information system (IS) is an important research topic in rough set theory (RST). This paper investigates attribute reduction for incomplete heterogeneous data based on information entropy. Information entropy in an incomplete IS with heterogeneous data (IISH) is first defined. Then, some derived notions of information entropy, such as joint information entropy, conditional information entropy, mutual information entropy, gain and gain ratio in an incomplete decision IS with heterogeneous data (IDISH), are presented. Next, information entropy is applied to perform attribute reduction in an IDISH. Two attribute reduction algorithms, based on gain and gain ratio, respectively, are proposed. Finally, in order to illustrate the feasibility and efficiency of the proposed algorithms, experimental analysis is carried out and comparisons are done. It is worth mentioning that the incomplete rate is used to deal with incomplete heterogeneous data.
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Affiliation(s)
- Pei Wang
- Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, Guangxi, P.R. China
| | - Liangdong Qu
- School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China
| | - Qinli Zhang
- School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, Anhui, P.R. China
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Qu L, He J, Zhang G, Xie N. Entropy measure for a fuzzy relation and its application in attribute reduction for heterogeneous data. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108455] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Li W, Pedrycz W, Xue X, Xu W, Fan B. Fuzziness and incremental information of disjoint regions in double-quantitative decision-theoretic rough set model. INT J MACH LEARN CYB 2018. [DOI: 10.1007/s13042-018-0893-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Qi J, Wei L, Wan Q. Multi-level granularity in formal concept analysis. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0112-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Chang TM, Hsu MF, Lin SJ. Integrated news mining technique and AI-based mechanism for corporate performance forecasting. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2017.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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