Interval-Valued Decision Information System via Information Structure

被引：0
|
作者：
Wu Y. [1 ,2 ]
Dai J. [1 ,2 ]
Chen J. [1 ,2 ]
机构：
[1] Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha
[2] College of Information Science and Engineering, Hunan Normal University, Changsha
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Fuzzy Rough Sets; Information Structure; Interval-Valued Data; KL Divergence; Uncertainty Measurement;
D O I：
10.16451/j.cnki.issn1003-6059.202008006
中图分类号：
学科分类号：
摘要：
Uncertainty measurement for single valued information system is widely studied. There are few researches on uncertainty measurement for interval-valued decision information system and the influence of the noise label on uncertainty measurement. Therefore, a robust uncertainty measurement for interval-valued decision information system via information structure is proposed. Firstly, the similarity degree between interval values is defined by KL divergence, and the fuzzy similarity relation of the interval values is constructed. Then, a information structure for interval-valued decision information system is proposed. In addition, K nearest neighbor points algorithm is introduced to calculate the membership degree of the samples about the decision, and two information structure based robust uncertainty measurement approaches are proposed to reduce the impact of noise labels on uncertainty measurement of systems. Finally, the validity and rationality of the proposed uncertainty measurement are verified through the experiments. © 2020, Science Press. All right reserved.
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页码：724 / 731
页数：7
相关论文
共 24 条
  • [1] WIERMAN M J., Measuring Uncertainty in Rough Set Theory, International Journal of General Systems, 28, pp. 283-297, (1996)
  • [2] LIANG J Y, QIAN Y H., Information Granules and Entropy Theory in Information Systems, Science in China(Information Sciences), 51, pp. 1427-1444, (2008)
  • [3] LI Z W, LI Q G, ZHANG R R., Knowledge Structures in a Know-ledge Base, Expert Systems, 33, 6, pp. 581-591, (2016)
  • [4] PAWLAK Z., Rough Sets: Theoretical Aspects of Reasoning about Data, (1991)
  • [5] MORRISSEY J M., Imprecise Information and Uncertainty in Information Systems, ACM Transactions on Information Systems, 8, 2, pp. 159-180, (1990)
  • [6] QIAN Y H, LIANG J Y, DANG C Y., Fuzzy Information Granularity in a Binary Granular Structure, IEEE Transactions on Fuzzy Systems, 19, 2, pp. 253-260, (2011)
  • [7] WANG C Z, YANG H, SHAO M W, Et al., Uncertainty Measures for Ge-neral Fuzzy Relations, Fuzzy Sets and Systems, 360, pp. 82-96, (2019)
  • [8] YAO S, CHEN J, WU Z Y., Combination Measurement Method Based on Neighborhood Tolerance Information Entropy, Journal of Chinese Computer Systems, 41, 1, pp. 46-50, (2020)
  • [9] HIANG G S, WEN H., Uncertainty Measures of Rough Set Based on Strictly Concave Functions, Journal of Software, 29, 11, pp. 3484-3499, (2018)
  • [10] ZHANG G Q, LI Z W, WU W Z, Et al., Information Structures and Uncertainty Measures in a Fully Fuzzy Information System, International Journal of Approximate Reasoning, 101, pp. 119-149, (2018)
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