Journal
IEEE ACCESS
Volume 8, Issue -, Pages 182908-182917Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3029157
Keywords
Information systems; Information entropy; Entropy; Computational modeling; Uncertainty; Rough sets; Measurement uncertainty; Covering information system; information entropy; multi-scale covering decision system; optimal scale combination
Categories
Funding
- National Natural Science Foundation of China [11701258, 11871259]
- Program for Innovative Research Team in Science and Technology in Fujian Province University
- Quanzhou High-Level Talents Support Plan [2017ZT012]
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In the traditional covering information systems, each object has only one scale, that is, the single-scale covering information system. But in realistic applications, one often needs to process and analyze data in different scales. To solve this problem, a new multi-scale covering information system is first introduced in this paper, and the optimal scale combination selections based on information entropy in multi-scale coveing decision systems are discussed. Firstly, entropy consistent scale combination and entropy optimal scale combination are defined in multi-scale covering information system. Secondly, entropy optimal scale combination is defined in multi-scale covering decision system, which is proved to be equivalent to the optimal scale combination in consistent multi-scale coveing decision system. Moreover, lower approximation entropy optimal scale combination is defined in the inconsistent multi-scale covering decision system. Finally, the corresponding selection algorithms and specific examples are given by using conditional entropy and limitary conditional entropy, respectively.
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