期刊信息

  • 刊名： 河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
  • 主办： 河北师范大学
  • ISSN： 1000-5854
  • CN： 13-1061/N
  • 中国科技核心期刊
  • 中国期刊方阵入选期刊
  • 中国高校优秀科技期刊
  • 华北优秀期刊
  • 河北省优秀科技期刊

协同采编系统

友情链接

协调的决策多尺度不完备信息系统的最优尺度选择

  • (1.浙江海洋大学 信息工程学院,浙江 舟山 316022;2.浙江省海洋大数据挖掘与应用重点实验室,浙江 舟山 316022)
  • DOI： 10.13763/j.cnki.jhebnu.nse.202201004

On Optimal Scale Selection in Consistent Incomplete Multi-scale Information Systems with Multi-scale Decisions

摘要/Abstract

摘要：

粒计算模拟人类思考模式,它以粒为基本计算单位,以建立大规模复杂数据和信息处理的有效计算模型为目标,是知识表示和数据挖掘的一个重要方法.针对决策属性具有多尺度的不完备数据集的知识获取问题,首先,提出了决策属性具有多尺度的广义不完备多尺度信息系统的最优尺度选择的概念,阐明了尺度选择全体构成了一个完备格;其次,给出了在不同尺度选择下信息粒的表示及其相互关系;最后,讨论了协调的决策多尺度不完备信息系统的最优尺度选择问题,并用示例解释最优尺度选择的计算.

Abstract：

Granular computing imitates human being's thinking. Its basic computing unit is called granules, and its objective is to establish effective computation models for dealing with large scale complex data and information. It is an important approach for knowledge representation and data mining. To investigate notion of optimal scale selections in generalized incomplete multi-scale information systems with multiscale decisions is firstly introduced. It is shown that the collection of all scale selections forms a complete lattice. Secondly, the representation of information granules under different scale selections and their relationships are given. Finally, optimal scale selections in consistent generalized incomplete multi-scale information systems with multi-scale decisions are discussed. And an example is also employed to explain the calculation of optimal scale selections.

关键词

关键词： 粒计算 ; 多尺度信息系统 ; 粗糙集 ; 最优尺度选择
Key words： granular computing ; multi-scale information systems ; rough sets ; optimal scale selection

参考文献 33

  • [1] 梁吉业, 钱宇华, 李德玉, 等. 大数据挖掘的粒计算理论与方法[J]. 中国科学:信息科学 ,2015, 45(11):1355-1369. doi:10.1360/n112015-00092 LIANG Jiye Y, QIAN Yuhua, LI Deyu Y,et al. Theory and Method of Granular Computing for Big Data Mining[J]. Scientia Sinica Informationis, 2015, 45(11):1355-1369.
  • [2] CHEN C L P, ZHANG C Y. Data-intensive Applications, Challenges, Techniques and Technologies:A Survey on Big Data[J]. Information Sciences, 2014, 275:314-347. doi:10.1016/j.ins.2014.01.015
  • [3] 吴伟志.多粒度粗糙集数据分析研究的回顾与展望[J]. 西北大学学报(自然科学版), 2018, 48(4):501-512.doi:10.16152/j.cnki.xdxbzr. 2018-04-003 WU W Z. Reviews and Prospects for the Study of Multi granularity Rough Set Data Analysis[J]. Journal of Northwest University (Natural Science Edition), 2018, 48(4):501-512.
  • [4] 徐计,王国胤,于洪.基于粒计算的大数据处理[J].计算机学报,2015,38(8):1497-1517. doi:10.11897/sp.j.1016.2015.0149 XU Ji, WANG Guoyin, YU Hong. Review of Big Data Processing Based on Granular Computing[J]. Chinese Journal of Computers, 2015, 38(8):1497-1517.
  • [5] PAWLAK Z. Rough Sets[J]. International Journal of Computer and Information Sciences, 1982, 11(5):341-356.doi:10.1007/bf01001956
  • [6] 郑嘉文,吴伟志,包菡,等.基于熵的多尺度决策系统的最优尺度选择[J].南京大学学报(自然科学版),2021,57(1):130-140.doi:10.13232/j.cnki.jnju.2021.01.001 ZHENG Jiawen, WU Weizhi, BAO Han, et al. Entropy Based Optimal Scale Selection for Multi-scale Decision Systems[J]. Journal of Nanjing University (Natural Science), 2021, 57(1): 130-140.
  • [7] 牛东苒,吴伟志,李同军.广义多尺度决策系统基于可变精度的最优尺度组合[J].模式识别与人工智能,2019, 32(11):965-974. doi:10.16451/j.cnki.issn1003-6059.201911001 NIU Dongran, WU Weizhi, LI Tongjun. Variable Precision Based Optimal Scale Combinations in Generalized Multi-scale Decision Systems[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(11): 965-974.
  • [8] 吴伟志,庄宇斌,谭安辉,等.不协调广义多尺度决策系统的尺度组合[J].模式识别与人工智能,2018,31(6):485-494.doi:10.16451/j.cnki.issn1003-6059.201806001 WU Weizhi, ZHUANG Yubin, TAN Anhui, et al. Scale Combinations in Inconsistent Generalized Multi scale Decision Systems[J]. Pattern Recognition and Artificial Intelligence, 2018,31(6): 485-494.
  • [9] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set[J]. Information Sciences, 2010, 180(6):949-970. doi:10.1016/j.ins. 2009.11.023
  • [10] QIAN Y H, LIANG J Y,DANG C Y. Incomplete Multi granulation Rough Set[J]. IEEE Transactions on Systems, Man and Cybernetics, 2010,40(2):420-431.doi:10.1109/tsmca. 2009.2035436
  • [11] ZHU P F, HU Q H, ZUO W M, et al. Multi granularity Distance Metric Learning via Neighborhood Granule Margin Maximization[J]. Information Sciences, 2014, 282:321-331. doi:10.1016/j.ins.2014.06.017
  • [12] ZHU P F,HU Q H. Adaptive Neighborhood Granularity Selection and Combination Based on Margin Distribution Optimization[J]. Information Sciences,2013,249:1-12.doi:10.1016/j.ins.2013.06.012
  • [13] WU W Z,LEUNG Y. Theory and Applications of Granular Labelled Partitions in Multi-scale Decision Tables[J]. Information Sciences, 2011,181(18):3878-3897. doi:10.1016/j.ins.2011.04.047
  • [14] 吴伟志, 杨丽, 谭安辉,等.广义不完备多粒度标记决策系统的粒度选择[J].计算机研究与发展, 2018, 55(6):1263-1272.doi:10.7544/issn1000-1239.2018.20170233 WU Weizhi, YANG Li, TAN Anhui, et al. Granularity Selections in Generalized Incomplete Multi-granular Labeled Decision Systems[J]. Journal of Computer Research and Development, 2018, 55(6): 1263-1272.
  • [15] LI F, HU B Q. A New Approach of Optimal Scale Selection to Multi-scale Decision Tables [J]. Information Sciences, 2017, 381:193-208. doi:10.1016/j.ins.2016.11.016
  • [16] WU W Z, LEUNG Y. Optimal Scale Selection for Multi-scale Decision Tables [J]. International Journal of Approximate Reasoning, 2013, 54(8):1107-129. doi:10.1016/j.ijar.2013.03.017
  • [17] SHE Y H, LI J H, YANG H L.A Local Approach to Rule Induction in Multi-scale Decision Tables [J]. Knowledge-based Systems, 2015, 89:398-410. doi:10.1016/j.knosys.2015.07.020
  • [18] 吴伟志,陈超君,李同军,等.不协调多粒度标记决策系统最优粒度的对比[J].模式识别与人工智能,2016,29(12):1095-1103.doi:10.16451/j. cnki. issn.1003-6059.201612006 WU Weizhi,CHEN Chaojun,LI Tongjun,et al. Comparative Study on Optimal Granularities in Inconsistent Multi-granular Labeled Decision Systems[J]. Pattern Recognition and Artificial Intelligence,2016,29(12):1095-1103.
  • [19] HAO C, LI J H, FAN M,et al. Optimal Scale Selection in Dynamic Multi-scale Decision Tables Based on Sequential Three-way Decisions[J]. Information Sciences, 2017,415:213-232. doi:10.1016/j.ins.2017.06.032
  • [20] LI F,HU B Q, WANG J. Stepwise Optimal Scale Selection for Multi-scale Decision Tables via Attribute Significance [J]. Knowledge-based Systems, 2017,129:4-16. doi:10.1016/j.knosys.2017.04.005
  • [21] XIE J P,YANG M H,LI J H,et al. Rule Acquisition and Optimal Scale Selection in Multi-scale Formal Decision Contexts and Their Applications to Smart City [J]. Future Generation Computer Systems, 2018, 83:564-581. doi:10.1016/j.future.2017.03.011
  • [22] LUO C, LI T R, HUNAG Y Y, et al. Updating Three-way Decisions in Incomplete Multi-scale Information Systems [J]. Information Sciences, 2019, 476:274-289. doi: 10.1016/j.ins.2018.10.012
  • [23] ZHANG X Q,ZHANG Q H, CHENG Y L, et al. Optimal Scale Selection by Integrating Uncertainty and Cost-sensitive Learning in Multi-scale Decision Tables [J]. International Journal of Machine Learning and Cybernetics, 2020, 11(5):1095-1114. doi: 10.1007/s13042-020-01101-x
  • [24] CHENG Y L, ZHANG Q H, WANG G Y, et al. Optimal Scale Selection and Attribute Reduction in Multi-scale Decision Tables Based on Three-way Decision [J]. Information Sciences, 2020,541:36-59. doi: 10.1016/j.ins.2020.05.109
  • [25] WU W Z, LEUNG Y. A Comparison Study of Optimal Scale Combination Selection in Generalized Multi-scale Decision Tables [J]. International Journal of Machine Learning and Cybernetics, 2020, 11(5): 961-972. doi: 10.1007/s13042-019-00954-1
  • [26] BAO H, WU W Z, ZHENG J W, et al. Entropy Based Optimal Scale Combination Selection for Generalized Multi-scale Information Tables [J]. International Journal of Machine Learning and Cybernetics, 2021, 12: 1427-1437. doi: 10.1007/s13042-020-01243-y
  • [27] CHENG Y L, ZHANG Q H, WANG G Y. Optimal Scale Combination Selection for Multi-scale Decision Tables Based on Three-way Decision [J]. International Journal of Machine Learning and Cybernetics, 2021, 12: 281-301. doi: 10.1007/s13042-020-01173-9
  • [28] HUANG B, LI H X, FENG G F, et al. Double-quantitative Rough Sets, Optimal Scale Selection and Reduction in Multi-scale Dominance IF Decision Tables[J]. International Journal of Approximate Reasoning, 2021, 130:170-191. doi: 10.1016/j.ijar.2020.12.001
  • [29] 张清华,张雪秋,庞国弘.多尺度决策系统中代价敏感的最优尺度组合[J].控制与决策,2021,36(10):2369-2378.doi:10.13195/j. kzyjc.2020.0121 ZHANG Qinghua,ZHANG Xueqiu,PANG Guohong. Cost-sensitive Optimal Scale Combination in Multi-scale Decision Systems[J]. Control and Decision,2021,36(10):2369-2378.
  • [30] HUANG Z H, LI J J, DAI W Z, et al.Generalized Multi-scale Decision Tables with Multi-scale Decision Attributes [J]. International Journal of Approximate Reasoning, 2019, 115:194-208. doi: 10.1016/j.ijar.2019.09.010
  • [31] KRYSZKIEWICZ M. Rough Set Approach to Incomplete Information Systems [J]. Information Sciences, 1998, 112:39-49. doi: 10.1016/s0020-0255(98)10019-1
  • [32] KRYSZKIEWICZ M. Rules in Incomplete Information Systems [J]. Information Sciences, 1999,113:271-292.doi:10.1016/s0020-0255(98)10065-8
  • [33] WU W Z, QIAN Y H, LI T J, et al. On Rule Acquisition in Incomplete Multi-scale Decision Tables [J]. Information Sciences, 2017, 378:282-302. doi: 10.1016/j.ins.2016.03.041

版权所有 2013-2021 河北师范大学学报编辑部 地址：河北省石家庄市南二环东路20号

邮编：050024 电话：0311-80786360 技术支持：载驰科技