[1]徐健锋,何宇凡,汤涛,等.概率粗糙集三支决策在线快速计算算法研究[J].智能系统学报,2018,13(05):741-750.[doi:10.11992/tis.201706047]
　XU Jianfeng,HE Yufan,TANG Tao,et al.Research on a fast online computing algorithm based on three-way decisions with probabilistic rough sets[J].CAAI Transactions on Intelligent Systems,2018,13(05):741-750.[doi:10.11992/tis.201706047]
点击复制

概率粗糙集三支决策在线快速计算算法研究(/HTML)
分享到：

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年05期
页码:
741-750
栏目:
出版日期:
2018-09-05

文章信息/Info

Title:
Research on a fast online computing algorithm based on three-way decisions with probabilistic rough sets
作者:
徐健锋1; 2;  何宇凡1;  汤涛1;  赵志宾1; 2
1. 南昌大学 软件工程系, 江西 南昌 330029;
2. 同济大学 计算机科学与技术系, 上海 201804
Author(s):
XU Jianfeng1; 2;  HE Yufan1;  TANG Tao1;  ZHAO Zhibin1; 2
1. Department of Software Engineering, Nanchang University, Nanchang 330029, China;
2. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
关键词:
三支决策; 粗糙集; 条件概率; 在线计算; 不确定; 动态计算; 粒计算
Keywords:
three-way decisions; rough sets; conditional probability; online computing; uncertain; dynamic calculation; granular computing
分类号:
TP181
DOI:
10.11992/tis.201706047
摘要:
随着大数据和物联网技术的不断发展，动态在线计算已经成为了一种常见的计算模式，在动态在线计算中进行不确定问题的推理和求解是一项具有挑战性的新议题。概率粗糙集三支决策理论是一种处理不确定性知识挖掘的有效工具，根据在线计算模式中数据同步增减的动态特点，提出了一种概率粗糙集三支决策的在线计算方法。首先，以内存滑动窗口模式对在线动态计算的数据变化特点进行理论建模；然后，根据上述模型中在线计算的数据变化模式，推导出不同类型数据变化模式下的三支决策条件概率及三支区域的变化规律；最后，提出了一种新型在线快速计算算法，其获取的三支决策规则与经典概率三支决策算法是等效的。通过与经典三支决策计算算法的多组对比实验，验证了提出的在线快速计算算法的高效性与稳定性。
Abstract:
With the continuous development of big data and IoT (Internet of Things), dynamic online computation has become a common computing pattern; however the field of dynamic online computation faces challenges in deducing and solving uncertainty problems. A three-way decision theory with probabilistic rough set method is an efficient tool for mining uncertain knowledge; thus a dynamic online computing approach of three-way decision theory with probabilistic rough set is proposed in this paper, in accordance with the features of data dynamic synchronization. First, a data model is established to describe the inherent features of dynamic online computation via memory sliding window mode. In terms of the variational features of dynamic online computation of the above model, a three-way decision conditional probability and the change rule of three-way area are deduced as diverse variational patterns of data. Finally, a novel algorithm of online rapid computation is proposed. The obtained three-way decision rule is identical with the three-way decision algorithm of classic probability. By comparison with the classic three-way decision algorithm through multiple experiments, the proposed online rapid computation algorithm is confirmed to have high efficiency and stability.

参考文献/References:

[1] FONG S, WONG R, VASILAKOS A V. Accelerated PSO swarm search feature selection for data stream mining big data[J]. IEEE transactions on services computing, 2016, 9(1):33-45.
[2] ZHANG Hong, LI Bo, JIANG Hongbo, et al. A framework for truthful online auctions in cloud computing with heterogeneous user demands[C]//Proceedings of 2013 IEEE INFOCOM. Turin, Italy, 2013:1510-1518.
[3] ESKANDARI S, JAVIDI M M. Online streaming feature selection using rough sets[J]. International journal of approximate reasoning, 2016, 69:35-57.
[4] CHAZELLE B, ROSENBERG B. The complexity of computing partial sums off-line[J]. International journal of computational geometry and applications, 1991, 1(1):33-45.
[5] DENG Jie, QU Zhiguo, ZHU Yongxu, et al. Towards efficient and scalable data mining using spark[C]//Proceedings of 2014 International Conference on Information and Communications Technologies. Nanjing, China, 2014:1-6.
[6] YAO Yiyu. Three-way decisions and cognitive computing [J]. Cognitive computation, 2016, 8(4):543-554.
[7] ZHOU Bing, YAO Yiyu, LUO Jigang. Cost-sensitive three-way email spam filtering[J]. Journal of intelligent information systems, 2014, 42(1):19-45.
[8] KHAN M T, AZAM N, KHALID S, et al. A three-way approach for learning rules in automatic knowledge-based topic models[J]. International journal of approximate reasoning, 2017, 82:210-226.
[9] LI Huaxiong, ZHANG Libo, ZHOU Xianzhong, et al. Cost-sensitive sequential three-way decision modeling using a deep neural network[J]. International journal of approximate reasoning, 2017, 85:68-78.
[10] QIAN Jin, DANG Chuangyin, YUE Xiaodong, et al. Attribute reduction for sequential three-way decisions under dynamic granulation[J]. International journal of approximate reasoning, 2017, 85:196-216.
[11] YU Hong, ZHANG Cong, WANG Guoyin. A tree-based incremental overlapping clustering method using the three-way decision theory[J]. Knowledge-based systems, 2016, 91:189-203.
[12] LUO Chuan, LI Tianrui, CHEN Hongmei, et al. Efficient updating of probabilistic approximations with incremental objects[J]. Knowledge-based systems, 2016, 109:71-83.
[13] NAUMAN M, AZAM N, YAO Jingtao. A three-way decision making approach to malware analysis using probabilistic rough sets[M]. New York, NY, USA:Elsevier, 2016.
[14] CHEN Yufei, YUE Xiaodong, FUJITA H, et al. Three-way decision support for diagnosis on focal liver lesions[J]. Knowledge-based systems, 2017, 127:85-99.
[15] CHEN Hongmei, LI Tianrui, LUO Chuan, et al. A decision-theoretic rough set approach for dynamic data mining[J]. IEEE transactions on fuzzy systems, 2015, 23(6):1958-1970.
[16] LIANG Decui, XU Zeshui, LIU Dun. Three-way decisions based on decision-theoretic rough sets with dual hesitant fuzzy information[J]. Information sciences, 2017, 396:127-143.
[17] ZHANG Hongying, YANG Shuyun, MA Jianmin. Ranking interval sets based on inclusion measures and applications to three-way decisions[J]. Knowledge-based systems, 2016, 91:62-70.
[18] ZHAO Xuerong, HU Baoqing. Fuzzy probabilistic rough sets and their corresponding three-way decisions[J]. Knowledge-based systems, 2016, 91:126-142.
[19] AZAM N, ZHANG Yan, YAO Jingtao. Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets[J]. European journal of operational research, 2017, 261(2):704-714.
[20] DENG Xiaofei, YAO Yiyu. A multifaceted analysis of probabilistic three-way decisions[J]. Fundamenta informaticae, 2014, 132(3):291-313.
[21] ZHANG Yan, YAO Jingtao. Gini objective functions for three-way classifications[J]. International journal of approximate reasoning, 2017, 81:103-114.
[22] GRECO S, MATARAZZO B, ROMAN S. Rough sets theory for multicriteria decision analysis[J]. European journal of operational research, 2001, 129(1):1-47.
[23] LIU Dun, LI Tianrui, ZHANG Junbo. Incremental updating approximations in probabilistic rough sets under the variation of attributes[J]. Knowledge-based systems, 2015, 73:81-96.
[24] LI Shaoyong, LI Tianrui, LIU Dun. Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set[J]. Knowledge-based systems, 2013, 40:17-26.
[25] ZHANG Junbo, LI Tianrui, RUAN Da, et al. Neighborhood rough sets for dynamic data mining[J]. International journal of intelligent systems, 2012, 27(4):317-342.
[26] LUO Chuan, LI Tianrui, CHEN Hongmei. Dynamic maintenance of three-way decision rules[C]//Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Shanghai, China, 2014:801-811.
[27] PAPANDREOU G, KOKKINOS I, SAVALLE P A. Modeling local and global deformations in Deep Learning:epitomic convolution, Multiple Instance Learning, and sliding window detection[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015:390-399.
[28] YUN U, LEE G. Sliding window based weighted erasable stream pattern mining for stream data applications[J]. Future generation computer systems, 2016, 59:1-20.

相似文献/References:

[1]尹林子,阳春华,桂卫华,等.规则分层约简算法[J].智能系统学报,2008,3(06):492.
　YIN Lin-zi,YANG Chun-hua,GUI Wei-hua,et al.Hierarchical reduction of rules[J].CAAI Transactions on Intelligent Systems,2008,3(05):492.
[2]毋 非,封化民,申晓晔.容错粗糙模型的事件检测研究[J].智能系统学报,2009,4(02):112.
　WU Fei,FENG Hua-min,SHEN Xiao-ye.Research on event detection based on the tolerance rough set model[J].CAAI Transactions on Intelligent Systems,2009,4(05):112.
[3]伞 冶,叶玉玲.粗糙集理论及其在智能系统中的应用[J].智能系统学报,2007,2(02):40.
　SAN Ye,YE Yu-ling.Rough set theory and its application in the intelligent systems[J].CAAI Transactions on Intelligent Systems,2007,2(05):40.
[4]王国胤,张清华,胡 军.粒计算研究综述[J].智能系统学报,2007,2(06):8.
　WANG Guo-yin,ZHANG Qing-hua,HU Jun.An overview of granular computing[J].CAAI Transactions on Intelligent Systems,2007,2(05):8.
[5]裴小兵,吴 涛,陆永忠.最小化决策规则集的计算方法[J].智能系统学报,2007,2(06):65.
　PEI Xiao-bing,WU Tao,LU Yong-zhong.Calculating method for a minimal set of decision rules[J].CAAI Transactions on Intelligent Systems,2007,2(05):65.
[6]张志飞,苗夺谦.基于粗糙集的文本分类特征选择算法[J].智能系统学报,2009,4(05):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
　ZHANG Zhi-fei,MIAO Duo-qian.Feature selection for text categorization based on rough set[J].CAAI Transactions on Intelligent Systems,2009,4(05):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
[7]马胜蓝,叶东毅.一种带禁忌搜索的粒子并行子群最小约简算法[J].智能系统学报,2011,6(02):132.
　MA Shenglan,YE Dongyi.A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability[J].CAAI Transactions on Intelligent Systems,2011,6(05):132.
[8]顾成杰,张顺颐,杜安源.结合粗糙集和禁忌搜索的网络流量特征选择[J].智能系统学报,2011,6(03):254.
　GU Chengjie,ZHANG Shunyi,DU Anyuan.Feature selection of network traffic using a rough set and tabu search[J].CAAI Transactions on Intelligent Systems,2011,6(05):254.
[9]周丹晨.采用粒计算的属性权重确定方法[J].智能系统学报,2015,10(02):273.[doi:10.3969/j.issn.1673-4785.201312008]
　ZHOU Danchen.A method for ascertaining the weight of attributes based on granular computing[J].CAAI Transactions on Intelligent Systems,2015,10(05):273.[doi:10.3969/j.issn.1673-4785.201312008]
[10]陈坚,陈健,邵毅明,等.粗糙集的过饱和多交叉口协同优化模型研究[J].智能系统学报,2015,10(5):783.[doi:10.11992/tis.201406045]
　CHEN Jian,CHEN Jian,SHAO Yiming,et al.Collaborative optimization model for oversaturated multiple intersections based on the rough set theory[J].CAAI Transactions on Intelligent Systems,2015,10(05):783.[doi:10.11992/tis.201406045]
[11]张楠,姜丽丽,岳晓冬,等.效用三支决策模型[J].智能系统学报,2016,11(4):459.[doi:10.11992/tis.201606010]
　ZHANG Nan,JIANG Lili,YUE Xiaodong,et al.Utility-based three-way decisions model[J].CAAI Transactions on Intelligent Systems,2016,11(05):459.[doi:10.11992/tis.201606010]
[12]苗夺谦,张清华,钱宇华,等.从人类智能到机器实现模型——粒计算理论与方法[J].智能系统学报,2016,11(6):743.[doi:10.11992/tis.201612014]
　MIAO Duoqian,ZHANG Qinghua,QIAN Yuhua,et al.From human intelligence to machine implementation model: theories and applications based on granular computing[J].CAAI Transactions on Intelligent Systems,2016,11(05):743.[doi:10.11992/tis.201612014]

备注/Memo

备注/Memo:
收稿日期:2017-06-12。
基金项目:国家自然科学基金项目（61763031，61673301）；上海市自然科学基金项目（14ZR1442600）；江西省研究生创新专项资金项目（YC2016-S053）.
作者简介:徐健锋,男,1973年生,副教授,博士研究生,计算机学会会员,主要研究方向为数据挖掘、粗糙集、机器学习。主持国家自然基金项目1项,参与国家自然科学基金项目2项;何宇凡,男,1994年生, 硕士研究生,主要研究方向为三支决策、粗糙集、粒计算、机器学习;汤涛,男,1993年生,硕士研究生,主要研究方向为粗糙集、粒计算、机器学习。
通讯作者:徐健锋.E-mail:jianfeng_x@ncu.edu.cn.
更新日期/Last Update: 2018-10-25