基于邻域决策系统的三因素融合属性约简
Journal: Advances in Computer and Autonomous Intelligence Research DOI: 10.12238/acair.v3i4.17925
Abstract
针对邻域粗糙集中属性重要度相同的多候选属性问题,本文提出了基于邻域决策系统的三因素融合属性约简方法。在邻域决策系统中,在属性依赖度的基础上,引入邻域粒距离以度量属性的分类差异性,结合交互信息来衡量条件属性间的组合互补性,设计了一个三因素融合属性约简算法。通过使用6个UCI公开数据集进行实验分析,结果表明所提算法能有效提升约简效率和约简集的分类准确率。
Keywords
属性约简；邻域决策系统；属性依赖度；邻域粒距离；交互信息
Funding
校级青年基金(25CAFUC05012)。
Full Text
PDF - Viewed/Downloaded: 0 TimesReferences
[1]BAUTISTA R,MILLAN M,DIAZ J F.An efficient implementa tion to calculate relative core and reducts[C].18th Internat ional Conference of the North American Fuzzy Information Pro cessing Society-NAFIPS. IEEE,1999:791-794.
[2]CUI S G, LI G S,SANG B B,el.Distance metric learningbased multi-granularity neighborhood rough sets for attrib ute reduction. Appl[J].Soft Comput,2024:159.
[3] 刘富,张潇,侯涛,等.基于粗糙集的基因信号属性约简[J]. 吉林大学学报(工学版),2015,45(2):624-629.
[4]张宇敬,王柳,齐晓娜,等.基于信息熵的商业银行客户画像属性约简研究[J].河北大学学报(自然科学版),2022,42(1):98-104.
[5] 周涛,陆惠玲,任海玲,等.基于粗糙集的属性约简算法综述[J].电子学报,2021,49(7):1439-1449.
[6] 姚晟,汪杰,徐风,陈菊.不一致邻域粗糙集的不确定性度量和属性约简[J].小型微型计算机系统,2018,39(4):700-706.
[7] 翟俊海,万丽艳,王熙照.最小相关性最大依赖度属性约简[J].计算机科学,2014,41(12):148-150,154.
[8] 毛华,赵书峰.最小相关性最大依赖度属性约简的改进算法[J].河北大学学报(自然科学版),2019,39(3):225-229.
[1]BAUTISTA R,MILLAN M,DIAZ J F.An efficient implementa tion to calculate relative core and reducts[C].18th Internat ional Conference of the North American Fuzzy Information Pro cessing Society-NAFIPS. IEEE,1999:791-794.
[2]CUI S G, LI G S,SANG B B,el.Distance metric learningbased multi-granularity neighborhood rough sets for attrib ute reduction. Appl[J].Soft Comput,2024:159.
[3]刘富,张潇,侯涛,等.基于粗糙集的基因信号属性约简[J].吉林大学学报(工学版),2015,45(2):624-629.
[4]张宇敬,王柳,齐晓娜,等.基于信息熵的商业银行客户画像属性约简研究[J].河北大学学报(自然科学版),2022,42(1):98-104.
[5]周涛,陆惠玲,任海玲,等.基于粗糙集的属性约简算法综述[J].电子学报,2021,49(7):1439-1449.
[6]姚晟,汪杰,徐风,陈菊.不一致邻域粗糙集的不确定性度量和属性约简[J].小型微型计算机系统,2018,39(4):700-706.
[7]翟俊海,万丽艳,王熙照.最小相关性最大依赖度属性约简[J].计算机科学,2014,41(12):148-150,154.
[8]毛华,赵书峰.最小相关性最大依赖度属性约简的改进算法[J].河北大学学报(自然科学版),2019,39(3):225-229.
[9]张清华,李新太,赵凡,等.基于信息粒度与交互信息的属性约简改进算法[J].闽南师范大学学报(自然科学版),2021,34(2):68-78.
[10]MIAO D,LI D.Rough sets theory algorithms and applicat ions[D].Press of Tsinghua University,2008.
[11]WILSON D R,MARTINEZ T R.Improved heterogeneous dist ance functions[J].Journal of Artificial Intelligence Research,1997,6(1):1-34.
[12]胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简[J].软件学报,2008,19(3):640-649.
[13]SLEZAK D.Approximate reducts in decision tables[C] //Proceedings of IPMU.1996,96:1159-1164.
[14]HU Q H,PAN W,AN S,et al.An efficient gene selection technique for cancer recognition based on neighborhood mu tual information[J].International Journal of Machine Learni ng and Cybernetics,2010,1(1-4):63-74.
[15]SUN L,XU J C,Feature selection using mutual informa tion based uncertainty measures for tumor classification[J].Bio-Medical Materials and Engineering,2014,24(1):763-770.
[16]QIAN Y H,LIANG J Y,DANG C Y,et al. Knowledge granula tion and knowledge distance in a knowledge base[J].Internati onal Journal of Approximate Reasoning,2011,19(2):263-264.
[17]BAY S D.The UCI KDD repository[J].http://kdd.ics.uci.edu.
[18]姚晟,汪杰,徐风,等.不完备邻域粗糙集的不确定性度量和属性约简[J].计算机应用,2018,38(1):97-103.
[19]HANG S,LI X,ZONG M,et al.Efficient kNN classification with different numbers of nearest neighbors[J].IEEE transact ions on neural networks and learning systems,2017,29(5):1774-1785.
[20]WANG R,LI W,LI R,et al.Automatic blur type classificat ion via ensemble SVM[J].Signal processing: image communicat ion,2019,71:24-35.
[1]Luo,L.The impact and implications of artificial intell igent-generated content (AIGC) on marketing campaigns based
on social media[J].SHS Web of Conferences,2024,207(ICDEBA 2024):02004.
[2]Stanikzai,M.E.,Mittal,E.Leveraging AI-generated and human-generated content for maximized user engagement in contentpreneurs’innovation and creativity[J].Journal of Inn
ovation and Entrepreneurship,2025,14(1):91.
[3]周瑶.符号消费视阈下AIGC驱动品牌节日营销创新研究[J].中国广告,2025,(07):80-85.
[4]廖秉宜,向蓓蓓.AI赋能下的品牌场景营销创新[J].现代广告,2021,(18):24-29.
[5]陈沛芹,陈瑜.人工智能焦虑对生成式人工智能采纳行为的影响机理——基于技术接受模型的实证研究[J/OL].新媒体与社会,2025,1-17.
[6]周艳.可供性视角下AIGC在品牌营销传播中的实践与隐忧[J].新闻前哨,2024,(08):17-19.
[2]CUI S G, LI G S,SANG B B,el.Distance metric learningbased multi-granularity neighborhood rough sets for attrib ute reduction. Appl[J].Soft Comput,2024:159.
[3] 刘富,张潇,侯涛,等.基于粗糙集的基因信号属性约简[J]. 吉林大学学报(工学版),2015,45(2):624-629.
[4]张宇敬,王柳,齐晓娜,等.基于信息熵的商业银行客户画像属性约简研究[J].河北大学学报(自然科学版),2022,42(1):98-104.
[5] 周涛,陆惠玲,任海玲,等.基于粗糙集的属性约简算法综述[J].电子学报,2021,49(7):1439-1449.
[6] 姚晟,汪杰,徐风,陈菊.不一致邻域粗糙集的不确定性度量和属性约简[J].小型微型计算机系统,2018,39(4):700-706.
[7] 翟俊海,万丽艳,王熙照.最小相关性最大依赖度属性约简[J].计算机科学,2014,41(12):148-150,154.
[8] 毛华,赵书峰.最小相关性最大依赖度属性约简的改进算法[J].河北大学学报(自然科学版),2019,39(3):225-229.
[1]BAUTISTA R,MILLAN M,DIAZ J F.An efficient implementa tion to calculate relative core and reducts[C].18th Internat ional Conference of the North American Fuzzy Information Pro cessing Society-NAFIPS. IEEE,1999:791-794.
[2]CUI S G, LI G S,SANG B B,el.Distance metric learningbased multi-granularity neighborhood rough sets for attrib ute reduction. Appl[J].Soft Comput,2024:159.
[3]刘富,张潇,侯涛,等.基于粗糙集的基因信号属性约简[J].吉林大学学报(工学版),2015,45(2):624-629.
[4]张宇敬,王柳,齐晓娜,等.基于信息熵的商业银行客户画像属性约简研究[J].河北大学学报(自然科学版),2022,42(1):98-104.
[5]周涛,陆惠玲,任海玲,等.基于粗糙集的属性约简算法综述[J].电子学报,2021,49(7):1439-1449.
[6]姚晟,汪杰,徐风,陈菊.不一致邻域粗糙集的不确定性度量和属性约简[J].小型微型计算机系统,2018,39(4):700-706.
[7]翟俊海,万丽艳,王熙照.最小相关性最大依赖度属性约简[J].计算机科学,2014,41(12):148-150,154.
[8]毛华,赵书峰.最小相关性最大依赖度属性约简的改进算法[J].河北大学学报(自然科学版),2019,39(3):225-229.
[9]张清华,李新太,赵凡,等.基于信息粒度与交互信息的属性约简改进算法[J].闽南师范大学学报(自然科学版),2021,34(2):68-78.
[10]MIAO D,LI D.Rough sets theory algorithms and applicat ions[D].Press of Tsinghua University,2008.
[11]WILSON D R,MARTINEZ T R.Improved heterogeneous dist ance functions[J].Journal of Artificial Intelligence Research,1997,6(1):1-34.
[12]胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简[J].软件学报,2008,19(3):640-649.
[13]SLEZAK D.Approximate reducts in decision tables[C] //Proceedings of IPMU.1996,96:1159-1164.
[14]HU Q H,PAN W,AN S,et al.An efficient gene selection technique for cancer recognition based on neighborhood mu tual information[J].International Journal of Machine Learni ng and Cybernetics,2010,1(1-4):63-74.
[15]SUN L,XU J C,Feature selection using mutual informa tion based uncertainty measures for tumor classification[J].Bio-Medical Materials and Engineering,2014,24(1):763-770.
[16]QIAN Y H,LIANG J Y,DANG C Y,et al. Knowledge granula tion and knowledge distance in a knowledge base[J].Internati onal Journal of Approximate Reasoning,2011,19(2):263-264.
[17]BAY S D.The UCI KDD repository[J].http://kdd.ics.uci.edu.
[18]姚晟,汪杰,徐风,等.不完备邻域粗糙集的不确定性度量和属性约简[J].计算机应用,2018,38(1):97-103.
[19]HANG S,LI X,ZONG M,et al.Efficient kNN classification with different numbers of nearest neighbors[J].IEEE transact ions on neural networks and learning systems,2017,29(5):1774-1785.
[20]WANG R,LI W,LI R,et al.Automatic blur type classificat ion via ensemble SVM[J].Signal processing: image communicat ion,2019,71:24-35.
[1]Luo,L.The impact and implications of artificial intell igent-generated content (AIGC) on marketing campaigns based
on social media[J].SHS Web of Conferences,2024,207(ICDEBA 2024):02004.
[2]Stanikzai,M.E.,Mittal,E.Leveraging AI-generated and human-generated content for maximized user engagement in contentpreneurs’innovation and creativity[J].Journal of Inn
ovation and Entrepreneurship,2025,14(1):91.
[3]周瑶.符号消费视阈下AIGC驱动品牌节日营销创新研究[J].中国广告,2025,(07):80-85.
[4]廖秉宜,向蓓蓓.AI赋能下的品牌场景营销创新[J].现代广告,2021,(18):24-29.
[5]陈沛芹,陈瑜.人工智能焦虑对生成式人工智能采纳行为的影响机理——基于技术接受模型的实证研究[J/OL].新媒体与社会,2025,1-17.
[6]周艳.可供性视角下AIGC在品牌营销传播中的实践与隐忧[J].新闻前哨,2024,(08):17-19.
Copyright © 2025 吴慧佳
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License