2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一种改进的案例推理分类方法研究

张春晓 严爱军 王普

张春晓, 严爱军, 王普. 一种改进的案例推理分类方法研究. 自动化学报, 2014, 40(9): 2015-2021. doi: 10.3724/SP.J.1004.2014.02015
引用本文: 张春晓, 严爱军, 王普. 一种改进的案例推理分类方法研究. 自动化学报, 2014, 40(9): 2015-2021. doi: 10.3724/SP.J.1004.2014.02015
ZHANG Chun-Xiao, YAN Ai-Jun, WANG Pu. An Improved Classification Approach by Case-based Reasoning. ACTA AUTOMATICA SINICA, 2014, 40(9): 2015-2021. doi: 10.3724/SP.J.1004.2014.02015
Citation: ZHANG Chun-Xiao, YAN Ai-Jun, WANG Pu. An Improved Classification Approach by Case-based Reasoning. ACTA AUTOMATICA SINICA, 2014, 40(9): 2015-2021. doi: 10.3724/SP.J.1004.2014.02015

一种改进的案例推理分类方法研究

doi: 10.3724/SP.J.1004.2014.02015
基金项目: 

国家自然科学基金(61374143)

详细信息
    作者简介:

    张春晓 北京工业大学电子信息与控制工程学院博士研究生.主要研究方向为案例推理及其应用.E-mail:zaichunfei123@emails.bjut.edu.cn

    通讯作者:

    严爱军 北京工业大学电子信息与控制工程学院副教授.2006年于东北大学获得博士学位.主要研究方向为人工智能,过程建模与优化控制,本文通信作者.E-mail:yanaijun@bjut.edu.cn

An Improved Classification Approach by Case-based Reasoning

Funds: 

Supported by National Natural Science Foundation of China (61374143)

  • 摘要: 特征属性的权重分配和案例检索策略对案例推理(Case-based reasoning,CBR)分类的准确率有显著影响. 本文提出一种结合遗传算法、内省学习和群决策思想改进的CBR分类方法. 首先,利用遗传算法得到多组属性权重,再根据内省学习原理对每组权重进行迭代调整;然后,通过案例群检索策略得到满足大多数原则的群决策分类结果;最后,以典型分类数据集的对比实验证明了本文方法能进一步提高CBR分类的准确率. 这表明内省学习可以保证权重分配的合理性,案例群检索策略能充分利用案例库的潜在信息,对提升CBR的学习能力有显著作用.
  • [1] Schank R C. Dynamic Memory: A Theory of Reminding and Learning in Computers and People. New York: Cambridge University Press, 1982.
    [2] Shokouhi S V, Skalle P, Aamodt A. An overview of case-based reasoning applications in drilling engineering. Artificial Intelligence Review, 2014, 41(3): 317-329
    [3] Aamodt A, Plaza E. Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications, 1994, 7(1): 39-59
    [4] Pian Jin-Xiang, Chai Tian-You, Li Jie-Jia. Application of case-based reasoning and iterative learning to laminar cooling process control. Acta Automatica Sinica, 2012, 38(12): 2032-2037(片锦香, 柴天佑, 李界家. 案例推理及迭代学习在层流冷却控制中的应用. 自动化学报, 2012, 38(12): 2032-2037)
    [5] Zhao Hong-Wei, Xie Yong-Fang, Jiang Zhao-Hui, Xu De-Gang, Yang Chun-Hua. An intelligently optimal setting approach based on froth features for level of flotation cells. Acta Automatica Sinica, 2014, 40(6): 1086-1097(赵洪伟, 谢永芳, 蒋朝辉, 徐德刚, 阳春华. 基于泡沫图像特征的浮选槽液位智能优化设定方法. 自动化学报, 2014, 40(6): 1086-1097)
    [6] Chattopadhyay S, Banerjee S, Rabhi F A, Acharya U R. A case-based reasoning system for complex medical diagnosis. Expert Systems, 2013, 30(1): 12-20
    [7] Yan A J, Wang W X,Zhang C X,Zhao H. A fault prediction method that uses improved case-based reasoning to continuously predict the status of a shaft furnace. Information Sciences, 2014, 259(2): 269-281
    [8] Lao S I, Choy K L, Ho G T S, Tsim Y C, Poon T C, Cheng C K. A real-time food safety management system for receiving operations in distribution centers. Expert Systems with Applications, 2012, 39(3): 2532-2548
    [9] Mantaras R L D, Plaza E. Case-based reasoning: an overview. AI Communication, 1997, 10(1): 21-29
    [10] Subramanyam Rallabandi V P, Sett S K. Knowledge-based image retrieval system. Knowledge-Based Systems, 2008, 21(2): 89-100
    [11] Chen Ling, Cheng Zhong-Hua, Zeng Hui-Yan. Study on case retrieval of case-based RCM analysis system. Computer and Engineering and Design, 2012, 33(2): 581-585 (陈凌, 程中华, 曾慧燕. 基于案例推理的RCM分析系统中案例检索研究. 计算机工程与设计, 2012, 33(2): 581-585)
    [12] Guo Xiao-Ping, Yuan Jie, Li Yuan. Feature space k nearest neighbor based batch process monitoring. Acta Automatica Sinica, 2014, 40(1): 135-142(郭小萍, 袁杰, 李元. 基于特征空间k最近邻的批次过程监视. 自动化学报, 2014, 40(1): 135-142)
    [13] Cover T M, Hart P E. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 1967, 13(1): 21-27
    [14] Ahn H, Kim K, Man I. Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system. Expert Systems, 2006, 23(5): 290-301
    [15] Kim M, Lee S, Woo S, Shin D H. Approximate cost estimating model for river facility construction based on case-based reasoning with genetic algorithms. KSCE Journal of Civil Engineering, 2012, 16(3): 283-292
    [16] Cox M T, Ram A. Introspective multistrategy learning: on the construction of learning strategies. Artificial Intelligence, 1999, 112(1-2): 1-55
    [17] Bonzano A, Cunningham P, Smyth B. Using introspective learning to improve retrieval in CBR: a case study in air traffic control. In: Proceeding of the 2nd International Conference on Case-Based Reasoning. Berlin Heidelberg, USA: Springer, 1997. 291-302
    [18] Zhang Z, Yang Q. Feature weight maintenance in case bases using introspective learning. Journal of Intelligent Information Systems, 2001, 16(2): 95-116
    [19] Zhang Gui-Mei, Jiang Shao-Bo, Chu Jun. Affine registration based on chord height point and genetic algorithm. Acta Automatica Sinica, 2013, 39(9): 1447-1457(张桂梅, 江少波, 储珺. 基于弦高点和遗传算法的仿射配准. 自动化学报, 2013, 39(9): 1447-1457)
    [20] Wan Jun, Xing Huan-Ge, Zhang Xiao-Hui. Algorithm of adjusting weights of decision-makers in multi-attribute group decision-making based on entropy theory. Control and Decision, 2010, 25(6): 907-910(万俊, 邢焕革, 张晓晖. 基于熵理论的多属性群决策专家权重的调整算法. 控制与决策, 2010, 25(6): 907-910)
    [21] Xu Jiu-Ping, Chen Jian-Zhong. The Theory and Methods of Group Decision Making with Its Realization. Beijing: Tsinghua University Press, 2009.(徐玖平, 陈建中. 群决策理论与方法及实现. 北京: 清华大学出版社, 2009.)
  • 加载中
计量
  • 文章访问数:  2092
  • HTML全文浏览量:  47
  • PDF下载量:  1046
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-05-29
  • 修回日期:  2014-05-15
  • 刊出日期:  2014-09-20

目录

    /

    返回文章
    返回