Computer Science ›› 2017, Vol. 44 ›› Issue (1): 84-89.doi: 10.11896/j.issn.1002-137X.2017.01.016

Previous Articles     Next Articles

Interval Parameters Optimization Model under Three-way Decisions Space and Its Application

LI Ming-xia, LIU Bao-xiang and ZHANG Chun-ying   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The interval concept lattice theory is a new method of mining objects based on interval parameters.It can more accurately deal with uncertain information.Interval parameters α and β can determine interval concepts and lattice structure,and then affect extracted decision rules.In order to solve the optimization problem of interval parameters,firstly we combined the theories of interval concept lattice and three-way decision-theoretic rough set,and then put forward three-way decision space theory.Secondly,according to the theory,the extension of interval concept was divided into positive region.Megative region and boundary region,moreover,the three-way decision rules and decision loss function were proposed.Through adjusting interval parameters,we can find more credible decision rules,and then optimize interval parameters.Finally we verified the model by an example.

Key words: Interval concept lattice,Three-way decision space,Loss function,Parameters optimization

[1] YAO Y Y.An outline of a theory of three-way decision[C]∥Proceedings of the 8th International RSCTC Conference.2012:1-17.
[2] YAO Y Y,WONG S K M,LINGRAS P.A decision-theoreticrough set model[C]∥The 5th International Symposium on Methodologies for Interlligent Systems.1990.
[3] YAO Y Y,WONG S K.A decision-theoretic framework for approximating concepts[J].International Journal of Man-Machine Studies,1992,7(6):793-809.
[4] YAO Y Y.Decision-theoretical rough set models[C]∥Procee-dings of Rough Sets Knowledge Technology(RSKT’07).2007:1-12.
[5] 李华雄,周献中,李天瑞,等.决策粗糙集理论及其研究进展[M].北京:科学出版社,2011.
[6] YAO J T,YAO Y Y,ZIARKO W.Probabilistic rough sets:approximations,decision-makings,and applications[J].International Journal of Approximate Reasoning,2008,9:253-254.
[7] YAO Y Y.Probabilistic rough sets approximation[J].International Journal of Approximate Reasoning,2008,9:255-271.
[8] 贾修一,商琳,周献中.三支决策理论与应用[M].南京:南京大学出版社,2010.
[9] LIU B X,ZHANG C Y.A new concept lattice structure:Interval concept lattice[J].Computer Science,2012,39(8):273-277.(in Chinese) 刘保相,张春英.一种新的概念格结构——区间概念格[J].计算机科学,2012,39(8):273-277.
[10] YAN H C,WANG H F,LIU B X.The structure characteristics and application of interval concept lattice [J].Microcomputer & Its Applications,2014,33(9):98-100.(in Chinese) 阎红灿,王会芳,刘保相.区间概念格的结构特性与应用[J].微型机与应用,2014,33(9):98-100.
[11] LI H X,LIU D,ZHOU X Z.A survey on decision-theoreticrough set [J].Journal of Chongqing University of Post and Te-lecommunications(Natural Science Edition),2010,2(5):624-630.(in Chinese) 李华雄,刘盾,周献中.决策粗糙集模型研究综述[J].重庆邮电大学学报(自然科学版),2010,2(5):624-630.
[12] JIA X Y,LI W,SHANG L,et al.An adaptive learning parameters algorithm in three-way decision-theoretic rough set model [J].Acta Electronica Sinica,2011,9(11):2520-2525.(in Chinese) 贾修一,李伟,商琳,等.一种自适应求三支决策中决策阈值的算法[J].电子学报2011,9(11):2520-2525.
[13] JIA X Y,SHANG L.A simulated annealing algorithm forlearning thresholds in three-way decision-theoretic rough set model [J].Journal of Chinese Computer System,2013,4(11):2604-2606.(in Chinese) 贾修一,商琳.一种求三支决策阈值的模拟退火算法[J].小型微型计算机系统,2013,4(11):2604-2606.
[14] CHEN G,LIU B Q,Wu Y.The new algorithm of optimal th-resholds for three-way decisions [J].Computer Application,2012,2(8):2212-2215.(in Chinese) 陈刚,刘秉权,吴岩.求三支决策最优阈值的新算法[J].计算机应用,2012,2(8):2212-2215.
[15] ZHANG C Y,WANG L Y.Incremental construction algorithm based on attribute power set for interval concept lattice[J].Application Research of Computers,2014(3):731-734.(in Chinese) 张春英,王立亚.基于属性集合幂集的区间概念格渐进式生成算法[J].计算机应用研究,2014(3):731-734.
[16] LI M X,ZHANG C Y,Wang L Y,et al.Parameters Optimization and Interval Concept Lattice Update with Change of Para-meters [J].ICIC Express Letters,2016,10(2):339-346.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .