A Comparative Study of Attribute Weights Assignment for Case-based Reasoning
-
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.Abstract: The attribute weights assignment in case-based reasoning (CBR) system may determine the similarities between cases, and thus it has a significant impact on the correctness of reasoning. To improve the reasoning performance, the water-filling theory is introduced to the attribute weights assignment in this paper. Reasonable indicators of weight distribution are established, an associated Lagrange function is constructed and the weight optimization solution can be achieved. Thereby a convergent water-filling assignment (WFA) algorithm is obtained which can be used in the weighted K-nearest neighbor rule to retrieve similar cases. Classification experiments for comparison between the mean assignment method, WFA method and genetic algorithms for the attribute weights using the 5-fold cross-validation method are conducted. The results show that the classification performance of CBR can be further increased after the attribute weights are assigned by WFA.
-
[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
点击查看大图
计量
- 文章访问数: 1999
- HTML全文浏览量: 126
- PDF下载量: 963
- 被引次数: 0