[1]程麟焰,胡峰.基于模糊超网络的知识获取方法研究[J].智能系统学报,2019,14(03):479-490.[doi:10.11992/tis.201804055]
 CHENG Linyan,HU Feng.Fuzzy hypernetwork-based knowledge acquisition method[J].CAAI Transactions on Intelligent Systems,2019,14(03):479-490.[doi:10.11992/tis.201804055]
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基于模糊超网络的知识获取方法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
479-490
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Fuzzy hypernetwork-based knowledge acquisition method
作者:
程麟焰12 胡峰12
1. 重庆邮电大学 计算机科学与技术学院, 重庆 400065;
2. 重庆邮电大学 计算智能重庆市重点实验室, 重庆 400065
Author(s):
CHENG Linyan12 HU Feng12
1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400
关键词:
模糊等价模糊集模糊粗糙集三支决策超网络知识获取方法分类算法
Keywords:
fuzzy equivalencefuzzy setfuzzy rough setthree-way decisionhypernetworksknowledge acquisition methodclassification algorithm
分类号:
TP18
DOI:
10.11992/tis.201804055
摘要:
本文结合模糊粗糙集理论与超网络的相关知识,提出了一种模糊超网络模型。与传统超网络模型的不同之处在于,模糊超网络模型采用了模糊等效关系来代替超网络中的分明等效关系,并在此基础上对超边的生成和演化进行了改进。根据样本的分布将样本集划分成3个区域,即正域、边界域和负域,不同区域的样本按照不同的方式生成超边;根据分类效果将超边集也划分成3个区域,并对不同区域的超边进行相应地替换处理。实验结果表明,在正确率、Precision、Recall等指标上,模糊超网络分类算法具有明显的优势。
Abstract:
Combining the fuzzy rough set theory with the related knowledge on hypernetworks, this paper proposes a fuzzy hypernetwork mode. In comparison with the traditional hypernetwork model, the fuzzy hypernetwork model uses the fuzzy equivalence relationship to replace the distinct equivalence relation in hypernetworks and then improves the generation and evolution of hyperedges on this basis. First, the samples are divided into three regions according to their distribution:positive, boundary, and negative regions. The samples of different regions generate hyperedges in different ways. Second, the hyperedges are also divided into three regions according to their classification results, and the corresponding replacement of hyperedges in different regions is implemented. The experimental results show that the fuzzy hypernetwork classification algorithm presents prominent advantages in terms of accuracy, precision, and recall, thus proving the validity of the classification algorithm.

参考文献/References:

[1] RIZA L S, JANUSZ A, BERGMEIR C, et al. Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets"[J]. Information sciences, 2014, 287:68-89.
[2] ZHANG Yu. Research on extension of the fuzzy rough set theory[J]. Advanced materials research, 2012, 532-533:1472-1476.
[3] 陈德刚. 模糊粗糙集理论与方法[M]. 北京:科学出版社, 2013.
[4] 王进, 朱文晓, 孙开伟, 等. 基于残差超网络的DNA微阵列数据分类[J]. 重庆邮电大学学报(自然科学版), 2015, 27(5):647-653 WANG Jin, ZHU Wenxiao, SUN Kaiwei, et al. Using residual hypernetwork for the classification of DNA microarray data[J]. Journal of Chongqing University of Posts and Telecommunications (natural science edition), 2015, 27(5):647-653
[5] 王进, 张军, 胡白帆. 结合最优类别信息离散的细粒度超网络微阵列数据分类[J]. 上海交通大学学报, 2013, 47(12):1856-1862 WANG Jin, ZHANG Jun, HU Baifan. Optimal class-dependent discretization-based fine-grain hypernetworks for classification of microarray data[J]. Journal of Shanghai Jiaotong University, 2013, 47(12):1856-1862
[6] 王进, 黄萍丽, 孙开伟, 等. 基于演化学习超网络的微阵列数据分类[J]. 江苏大学学报(自然科学版), 2014, 35(1):56-62 WANG Jin, HUANG Pingli, SUN Kaiwei, et al. Microarray data classification based on evolutionary learning hypernetwork[J]. Journal of Jiangsu University (natural science edition), 2014, 35(1):56-62
[7] 王进, 丁凌, 孙开伟, 等. 演化超网络在多类型癌症分子分型中的应用[J]. 电子与信息学报, 2013, 35(10):2425-2431 WANG Jin, DING Ling, SUN Kaiwei, et al. Applying evolutionary hypernetworks for multiclass molecular classification of cancer[J]. Journal of electronics and information technology, 2013, 35(10):2425-2431
[8] 王进, 金理雄, 孙开伟. 基于演化超网络的中文文本分类方法[J]. 江苏大学学报(自然科学版), 2013, 34(2):196-201 WANG Jin, JIN Lixiong, SUN Kaiwei. Chinese text categorization based on evolutionary hypernetwork[J]. Journal of Jiangsu University (natural science edition), 2013, 34(2):196-201
[9] 王进, 孙开伟, 李钟浩. 超网络道路限速标志识别[J]. 小型微型计算机系统, 2012, 33(12):2709-2714 WANG Jin, SUN Kaiwei, LI Zhonghao. Hypernetworks for road speed limit sign recognition[J]. Journal of Chinese computer systems, 2012, 33(12):2709-2714
[10] 齐亚丽. 基于模糊粗糙集属性约简方法的研究[D]. 锦州:渤海大学, 2016. QI Yali. The research of attribute reduction method based on fuzzy rough sets[D]. Jinzhou:Bohai University, 2016.
[11] LI Xingyi, LI Xueling, SHI Huaji. Case based reasoning based on fuzzy rough set[C]//Proceedings of the 2nd IEEE International Conference on Information and Financial Engineering. Chongqing, China, 2010:778-782.
[12] 王世强, 张登福, 毕笃彦, 等. 基于模糊粗糙集和蜂群算法的属性约简[J]. 中南大学学报(自然科学版), 2013, 44(1):172-178 WANG Shiqiang, ZHANG Dengfu, BI Duyan, et al. Attribute reduction method based on fuzzy rough sets and artificial bee colony algorithm[J]. Journal of Central South University (science and technology), 2013, 44(1):172-178
[13] WANG Xueen, HAN Deqiang, HAN Chongzhao. Fuzzy-rough set based attribute reduction with a simple fuzzification method[C]//Proceedings of the 24th Chinese Control and Decision Conference. Taiyuan, China, 2012:3793-3797.
[14] HU Feng, SHI Jin. Neighborhood hypergraph based classification algorithm for incomplete information system[J/OL]. Mathematical problems in engineering, 2015, Article ID 735014, DOI:10. 1155/2015/735014.
[15] KOHAVI R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C]//Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, CA, USA, 1995:1137-1143.
[16] HU Feng, LIU Xiao, LU Xi. A novel cost sensitive classification algorithm based on neighborhood hypergraph[J]. Journal of computational information systems, 2015, 11(1):109-121.
[17] HU Feng, LI Hang. A novel boundary oversampling algorithm based on neighborhood rough set model:NRSBoundary-SMOTE[J]. Mathematical problems in engineering, 2013, 2013:694809.
[18] LUO Yueguo, XIONG Zhongyang, XIA Shuyin, et al. Classification noise detection based SMO algorithm[J]. Optik-international journal for light and electron optics, 2016, 127(17):7021-7029.
[19] 路敦利, 宁芊, 臧军. 基于BP神经网络决策的KNN改进算法[J]. 计算机应用, 2017(S2):65-67 LU Dunli, NING Qian, ZANG Jun. Improved KNN algorithm based on BP neural network decision making[J]. Journal of computer applications, 2017(S2):65-67
[20] 袁梅宇. 数据挖掘与机器学习:WEKA应用技术与实践[M]. 2版. 北京:清华大学出版社, 2014.

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备注/Memo

备注/Memo:
收稿日期:2018-04-26。
基金项目:国家自然科学基金项目(61533020,61472056,61309014);重点产业共性关键技术创新专项项目(cstc2017zdcy-zdyf0332,cstc2017zdcy-zdzx0046);重庆市基础与前沿项目(cstc2017jcyjAX0408).
作者简介:程麟焰,女,1993年生,硕士研究生,主要研究方向为机器学习与数据挖掘;胡峰,男,1978年生,教授,博士,主要研究方向为数据挖掘、Rough集和粒计算。发表学术论文40余篇,被SCI、EI检索20余篇。
通讯作者:程麟焰.E-mail:496732322@qq.com
更新日期/Last Update: 1900-01-01