[1]孙海霞.基于对象变化的邻域决策粗糙集动态更新算法[J].智能系统学报,2021,16(4):746-756.[doi:10.11992/tis.202010028]
SUN Haixia.Dynamic updating algorithm of neighborhood decision-theoretic rough set model based on object change[J].CAAI Transactions on Intelligent Systems,2021,16(4):746-756.[doi:10.11992/tis.202010028]
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SUN Haixia.Dynamic updating algorithm of neighborhood decision-theoretic rough set model based on object change[J].CAAI Transactions on Intelligent Systems,2021,16(4):746-756.[doi:10.11992/tis.202010028]
基于对象变化的邻域决策粗糙集动态更新算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第4期
页码:
746-756
栏目:
学术论文—知识工程
出版日期:
2021-07-05
- Title:
- Dynamic updating algorithm of neighborhood decision-theoretic rough set model based on object change
- Keywords:
- rough set; decision-theoretic rough set; neighborhood; incremental learning; approximation set; object; iteration; dynamic
- 分类号:
- TP18
- 摘要:
- 针对现实环境下数据集不断动态变化的特性，提出一种邻域决策粗糙集模型的增量式更新算法。采用由简单到复杂的研究思路，分析了邻域型信息系统论域增加和减少单个对象时，目标近似集与邻域类之间概率的变化规律，进一步地利用这种规律来构造单个对象变化时邻域决策粗糙集模型上下近似集的增量式更新，在单个对象变化的基础上，通过逐步迭代的方式设计了对象批量变化时的增量式更新算法。实验分析表明，所提出的算法具有较高的增量式更新性能，适用于动态数据环境下邻域决策粗糙集模型的动态更新。
- Abstract:
- In accordance with the dynamic characteristics of a dataset in a real environment, an incremental updating algorithm of the neighborhood decision-theoretic rough set model is proposed after conducting simple to complex research. In this paper, the change law of the probability between the target approximation set and the neighborhood class is first analyzed in the context of the domain of the neighborhood information system increasing or decreasing individual objects, which is then adopted to construct the incremental updating of the upper and lower approximation sets of the neighborhood decision-theoretic rough set model. On the basis of the change of a single object and given the variety of multiple objects, an incremental updating algorithm is designed through step-by-step iteration. Experimental results show that the proposed algorithm has a high incremental updating performance, thereby making it suitable for the dynamic updating of the neighborhood decision-theoretic rough set model in a dynamic data environment.
- 参考文献/References:
-
[1] YAO Yiyu. Three-way decisions with probabilistic rough sets[J]. Information sciences, 2010, 180(3): 341-353.
[2] YAO Yiyu. The superiority of three-way decisions in probabilistic rough set models[J]. Information sciences, 2011, 181(6): 1080-1096.
[3] 陈家俊, 徐华丽, 魏赟. 多重代价多粒度决策粗糙集模型研究[J]. 计算机科学与探索, 2018, 12(5): 839-850
CHEN Jiajun, XU Huali, WEI Yun. Multi-cost based multi-granulation decision-theoretic rough set model[J]. Journal of frontiers of computer science and technology, 2018, 12(5): 839-850
[4] JIA Xiuyi, LI Weiwei, SHANG Lin. A multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model[J]. Information sciences, 2019, 485: 248-262.
[5] 张婷, 张红云, 王真. 基于三支决策粗糙集的迭代量化的图像检索算法[J]. 南京大学学报(自然科学版), 2018, 54(4): 714-724
ZHANG Ting, ZHANG Hongyun, WANG Zhen. image retrieval: Iterative quantization based on saliency detection and three-way decision based rough sets[J]. Journal of Nanjing University (natural sciences edition), 2018, 54(4): 714-724
[6] ZHAO Xuerong, HU Baoqing. Three-way decisions with decision-theoretic rough sets in multiset-valued information tables[J]. Information sciences, 2020, 507: 684-699.
[7] LIU Dun, LIANG Decui, WANG Changchun. A novel three-way decision model based on incomplete information system[J]. Knowledge-based systems, 2016, 91: 32-45.
[8] SUN Bingzhen, MA Weimin, ZHAO Haiyan. Decision-theoretic rough fuzzy set model and application[J]. Information sciences, 2014, 283: 180-196.
[9] DOU Huili, YANG Xibei, SONG Xiaoning, et al. Decision-theoretic rough set: a multicost strategy[J]. Knowledge-based systems, 2016, 91: 71-83.
[10] LI Weiwei, HUANG Zhiqiu, JIA Xiuyi, et al. Neighborhood based decision-theoretic rough set models[J]. International journal of approximate reasoning, 2016, 69: 1-17.
[11] SHU Wenhao, QIAN Wenbin, XIE Yonghong. Incremental approaches for feature selection from dynamic data with the variation of multiple objects[J]. Knowledge-based systems, 2019, 163: 320-331.
[12] 段海玲, 王光琼. 一种高效的复杂信息系统增量式属性约简[J]. 华南理工大学学报(自然科学版), 2019, 47(6): 18-30
DUAN Hailing, WANG Guangqiong. An efficient incremental attribute reduction for complex information systems[J]. Journal of South China University of Technology (natural science edition), 2019, 47(6): 18-30
[13] HU Chengxiang, ZHANG Li, WANG Bangjun, et al. Incremental updating knowledge in neighborhood multigranulation rough sets under dynamic granular structures[J]. Knowledge-based systems, 2019, 163: 811-829.
[14] ZHANG Qinghua, LV Gongxun, CHEN Yuhong, et al. A dynamic three-way decision model based on the updating of attribute values[J]. Knowledge-based systems, 2018, 142: 71-84.
[15] XU Jianfeng, MIAO Duoqian, ZHANG Yuanjian, et al. A three-way decisions model with probabilistic rough sets for stream computing[J]. International journal of approximate reasoning, 2017, 88: 1-22.
[16] 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.
[17] 赵小龙, 杨燕. 基于邻域粒化条件熵的增量式属性约简算法[J]. 控制与决策, 2019, 34(10): 2061-2072
ZHAO Xiaolong, YANG Yan. Incremental attribute reduction algorithm based on neighborhood granulation conditional entropy[J]. Control and decision, 2019, 34(10): 2061-2072
[18] 杨臻, 邱保志. 混合信息系统的动态变精度粗糙集模型[J]. 控制与决策, 2020, 35(2): 297-308
YANG Zhen, QIU Baozhi. Dynamic variable precision rough set model of mixed information system[J]. Control and decision, 2020, 35(2): 297-308
[19] LUO Chuan, LI Tianrui, ZHANG Yi, et al. Matrix approach to decision-theoretic rough sets for evolving data[J]. Knowledge-based systems, 2016, 99: 123-134.
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备注/Memo
收稿日期:2020-10-26。
基金项目:安徽省质量工程项目(2020ZYRC063，2020XSKT151)；省教育厅高校自然科学重点项目(KJ2019A0891)
作者简介:孙海霞，副教授，CCF会员，主要研究方向为数据挖掘和网络安全。主持安徽省质量工程项目2项。发表学术论文10余篇
通讯作者:孙海霞.E-mail:wangbo19855@163.com
更新日期/Last Update:
1900-01-01