Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation

Fan Bingbing; Li Jin; Chen Xicheng; Liu Mengbo; Gu Jinghao; Liu Ming

doi:doi:10.11648/j.sd.20180604.20

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Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation

Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, Liu Ming

Published in Science Discovery (Volume 6, Issue 4)

Received: 9 August 2018 Published: 10 August 2018

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Abstract

In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.

Published in	Science Discovery (Volume 6, Issue 4)
DOI	10.11648/j.sd.20180604.20
Page(s)	290-297
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Maximal Consistent Relation, Multi-Granulation, Decision–Theoretic Rough Set, Classification Accuracy

References

[1]	Pawlak Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982, 11(5):341-356.
[2]	KRYSZKIEWICZ M. Rough set approach to incomplete information systems[J]. Information Sciences, 1998, 112(1-4):39-49.
[3]	曾玲,付敏,何普彦.双精度容差关系的粗糙集拓展模型及约简[J].江苏大学学报:自然科学版,2014,35(3):312-318.
[4]	Zhang xin, Li Xuwu. Decision-Theoretic Rough Set Based on Incomplete Interval-Valued Information System[J]. Application Research of Computers,2017,34(1):110-113.
[5]	Yao Y Y. Decision-theoretic rough set models[J].LNAI4481,2007:1-12.
[6]	Qian Y H, Liang J Y, Yao Y Y, et al. MGRS: a multi-granulation routh set[J]. Information Sciences, 2010, 180(6):949-970.
[7]	Decui Liang, Dun Liu, Witold Pedrycz, Pei Hu. Triangular fuzzy decision-theoretic rough sets[J]. International Journal of Approximate Reasoning, 2013,54:1087-1106.
[8]	Yiyu Yao, Yan Zhao. Attribute Reduction in Decision-Theoretic Rough Set Models[J]. Information Science, 2008,178(17):3356-3373.
[9]	Xiuyi Jia, Wenhe Liao, Zhenmin Tang, Lin Shang. Minimum cost attribute reduction in decision-theoretic rough set models[J]. Information Science,2013,219:151-167.
[10]	Liu D, Li TR, Li HX.A multiple-category classification approach with decision-theoretic rough sets. Fundamenta Informaticae， 2012， 115（2-3）： 173-188.
[11]	Li Feng, Miao Duoqian, Liu Chaihui，et al. Image segmentation algorithm based on the decision-theoretic rough set model[J].CAAI Transactions on Intelligent Systems,2014,9(2): 143- 147.
[12]	Yang Xiaoping, Yao Jingtao. A Multi-agent Decision-Theoretic Rough Set Model [J]. Lecture Notes in Computer Science, 2010, 6401(2):711-718.
[13]	Yang Xiaoping, Yao Jingtao.Modeling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets[J]. Fundamenta Informaticae, 2012, 115(2-3): 157-171.
[14]	Ma Xinbin, Ju Hengrong, Yang Xibei, et al. Multi-cost based decision-theoretic rough sets in incomplete information systems[J].Journal of Nanjing University (Natural Sciences), 2015, 51(2): 335-342.
[15]	Dou Huili, Yang Xibei, Song Xiaoning, et al. Decision-theoretic rough set: A multicost strategy[J]. Knowledge-Based Systems, 2016(91):71-83.
[16]	Yu Hong, Wang Guoying, Yao Yiyu. Current research and future perspectives on decision-theoretic rough sets[J] Chinese journal of computers, 2015,38(8):1628-1639.
[17]	Li Feng, Miao Duoqian, Liu Chaihui，et al. Image segmentation algorithm based on the decision-theoretic rough set model[J].CAAI Transactions on Intelligent Systems,2014,9(2): 143- 147.
[18]	Lafferty,J, McCallum A, Pereira F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proc of the 18th lnternational Conference on Machine Learning. Sanfrancisco:Margan Kaufmann,2001:282-289.

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APA Style

Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, et al. (2018). Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Science Discovery, 6(4), 290-297. https://doi.org/10.11648/j.sd.20180604.20

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ACS Style

Fan Bingbing; Li Jin; Chen Xicheng; Liu Mengbo; Gu Jinghao, et al. Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Sci. Discov. 2018, 6(4), 290-297. doi: 10.11648/j.sd.20180604.20

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AMA Style

Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, et al. Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Sci Discov. 2018;6(4):290-297. doi: 10.11648/j.sd.20180604.20

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@article{10.11648/j.sd.20180604.20,
  author = {Fan Bingbing and Li Jin and Chen Xicheng and Liu Mengbo and Gu Jinghao and Liu Ming},
  title = {Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation},
  journal = {Science Discovery},
  volume = {6},
  number = {4},
  pages = {290-297},
  doi = {10.11648/j.sd.20180604.20},
  url = {https://doi.org/10.11648/j.sd.20180604.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20180604.20},
  abstract = {In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.},
 year = {2018}
}

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TY  - JOUR
T1  - Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation
AU  - Fan Bingbing
AU  - Li Jin
AU  - Chen Xicheng
AU  - Liu Mengbo
AU  - Gu Jinghao
AU  - Liu Ming
Y1  - 2018/08/10
PY  - 2018
N1  - https://doi.org/10.11648/j.sd.20180604.20
DO  - 10.11648/j.sd.20180604.20
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 290
EP  - 297
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20180604.20
AB  - In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.
VL  - 6
IS  - 4
ER  -

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Author Information

Fan Bingbing

Graduate College Air Force Engineering University, Xi’an, China
Li Jin

Graduate College Air Force Engineering University, Xi’an, China
Chen Xicheng

Graduate College Air Force Engineering University, Xi’an, China
Liu Mengbo

Graduate College Air Force Engineering University, Xi’an, China
Gu Jinghao

Graduate College Air Force Engineering University, Xi’an, China
Liu Ming

Graduate College Air Force Engineering University, Xi’an, China

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Plain Text BibTeX RIS

APA Style

Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, et al. (2018). Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Science Discovery, 6(4), 290-297. https://doi.org/10.11648/j.sd.20180604.20

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ACS Style

Fan Bingbing; Li Jin; Chen Xicheng; Liu Mengbo; Gu Jinghao, et al. Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Sci. Discov. 2018, 6(4), 290-297. doi: 10.11648/j.sd.20180604.20

Copy | Download

AMA Style

Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, et al. Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Sci Discov. 2018;6(4):290-297. doi: 10.11648/j.sd.20180604.20

Copy | Download

@article{10.11648/j.sd.20180604.20,
  author = {Fan Bingbing and Li Jin and Chen Xicheng and Liu Mengbo and Gu Jinghao and Liu Ming},
  title = {Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation},
  journal = {Science Discovery},
  volume = {6},
  number = {4},
  pages = {290-297},
  doi = {10.11648/j.sd.20180604.20},
  url = {https://doi.org/10.11648/j.sd.20180604.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20180604.20},
  abstract = {In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation
AU  - Fan Bingbing
AU  - Li Jin
AU  - Chen Xicheng
AU  - Liu Mengbo
AU  - Gu Jinghao
AU  - Liu Ming
Y1  - 2018/08/10
PY  - 2018
N1  - https://doi.org/10.11648/j.sd.20180604.20
DO  - 10.11648/j.sd.20180604.20
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 290
EP  - 297
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20180604.20
AB  - In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.
VL  - 6
IS  - 4
ER  -

Copy | Download