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案例推理属性权重的分配模型比较研究

严爱军 钱丽敏 王普

严爱军, 钱丽敏, 王普. 案例推理属性权重的分配模型比较研究. 自动化学报, 2014, 40(9): 1896-1902. doi: 10.3724/SP.J.1004.2014.01896
引用本文: 严爱军, 钱丽敏, 王普. 案例推理属性权重的分配模型比较研究. 自动化学报, 2014, 40(9): 1896-1902. doi: 10.3724/SP.J.1004.2014.01896
YAN Ai-Jun, QIAN Li-Min, WANG Pu. A Comparative Study of Attribute Weights Assignment for Case-based Reasoning. ACTA AUTOMATICA SINICA, 2014, 40(9): 1896-1902. doi: 10.3724/SP.J.1004.2014.01896
Citation: YAN Ai-Jun, QIAN Li-Min, WANG Pu. A Comparative Study of Attribute Weights Assignment for Case-based Reasoning. ACTA AUTOMATICA SINICA, 2014, 40(9): 1896-1902. doi: 10.3724/SP.J.1004.2014.01896

案例推理属性权重的分配模型比较研究

doi: 10.3724/SP.J.1004.2014.01896
基金项目: 

国家自然科学基金项目(61374143)

详细信息
    作者简介:

    严爱军 北京工业大学电子信息与控制工程学院副教授.2006年获东北大学博士学位.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

    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年获东北大学博士学位.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

A Comparative Study of Attribute Weights Assignment for Case-based Reasoning

Funds: 

Supported by National Natural Science Foundation of China (61374143)

  • 摘要: 案例推理系统中各属性权重的赋值决定了案例之间的相似度 大小,进而对推理结果的正确与否产生显著影响.以属性加权K-最近邻 相似案例检索为基础,讨论了使用注水原理分配属性权重的机理,并通过建 立权重分配的合理性指标,构造拉格朗日函数对权重进行优 化求解,得到了收敛的注水分配算法.通过五折交叉的模式分类实验 ,分别对属性权重的平均分配法、注水分配算法和遗传算法分配法进行了比较研究,案例推理分类结果证明,在引入注水分配算法后,其分类性能得到有效改善.
  • [1] Schank R C. Dynamic Memory: A Theory of Reminding and Learning in Computers and People. New York: Cambridge University Press, 1982.
    [2] Kolodner J L. Maintaining organization in a dynamic long-term memory. Cognitive Science, 1983, 7(4): 243-280
    [3] Aamodt A, Plaza E. Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications, 1994, 7(1): 39-59
    [4] Liu Y H, Yang C S, Yang Y B, Lin F H, Du X M, Ito T. Case learning for CBR-based collision avoidance systems. Applied Intelligence, 2012, 36(2): 308-319
    [5] Xing G S, Ding J L, Chai T Y, Afshar P, Wang H. Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process. Engineering Applications of Artificial Intelligence, 2012, 25(2): 418-429
    [6] Chai Tian-You. Operational optimization and feedback control for complex industrial processes. Acta Automatica Sinica, 2013, 39(11): 1744-1757 (柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744-1757)
    [7] Tadrat J, Boonjing V, Pattaraintakorn P. A new similarity measure in formal concept analysis for case-based reasoning. Expert Systems with Applications, 2012, 39(1): 967-972
    [8] Wang H C, Huang T H. An enhanced case-based reasoning model for supporting inference missing attribute and its feature weight. Journal of Internet Technology, 2012, 13(1): 45-56
    [9] Carmona M A, Barbancho J, Larios D F, León C. Applying case based reasoning for prioritizing areas of business management. Expert Systems with Applications, 2013, 40(9): 3450-3458
    [10] Cover T M, Hart P E. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 1967, 13(1): 21-27
    [11] Lin S W, Chen S C. Parameter tuning, feature selection and weight assignment of features for case-based reasoning by artificial immune system. Applied Soft Computing Journal, 2011, 11(8): 5042-5052
    [12] Kim K J, Kim K. Preliminary cost estimation model using case-based reasoning and genetic algorithms. Journal of Computing in Civil Engineering, 2010, 24(6): 499-505
    [13] Kristi R, Qiang Y. Redundancy detection in semi-structured case bases. IEEE Transactions on Knowledge and Data Engineering, 2001, 13(3): 513-518
    [14] Park C S, Han I. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 2002, 23(3): 255-264
    [15] 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
    [16] Pian Jin-Xiang, Chai Tian-You, Li Jie-Jia. Application of case-based reasoning and iterative learning in 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)
    [17] Pian Jin-Xiang, Chai Tian-You, Li Jie-Jia. Rule and data driven strip coiling temperature model in laminar cooling process. Acta Automatica Sinica, 2012, 38(11): 1861-1869 (片锦香, 柴天佑, 李界家. 规则与数据驱动的层流冷却过程带钢卷取温度模型. 自动化学报, 2012, 38(11): 1861-1869)
    [18] Mishra N, Petrovic S, Sundar S. A self-adaptive case-based reasoning system for dose planning in prostate cancer radiotherapy. Medical Physics, 2011, 38(12): 6528-6538
    [19] Bergmann R, Kolodner J, Plaza E. Representation in case-based reasoning. Knowledge Engineering Review, 2005, 20(3): 209-213
    [20] Liu C H, Chen H C. A novel CBR system for numeric prediction. Information Sciences, 2012, 185(1): 178-190
    [21] Kaedi M, Ghasem-Aghaee N. Biasing Bayesian optimization algorithm using case based reasoning. Knowledge-Based Systems, 2011, 24(8): 1245-1253
    [22] Lopez de Mantaras R, Plaza E. Case-based reasoning: an overview. AI Communications, 1997, 10(1): 21-29
    [23] Luo B, Cui Q M, Wang H, Tao X F. Optimal joint water-filling for coordinated transmission over frequency-selective fading channels. IEEE Communications Letters, 2011, 15(2): 190-192
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出版历程
  • 收稿日期:  2013-05-29
  • 修回日期:  2014-02-26
  • 刊出日期:  2014-09-20

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