案例推理分类器属性权重的内省学习调整方法

doi:10.11772/j.issn.1001-9081.2014.08.2273

计算机应用 ›› 2014, Vol. 34 ›› Issue (8): 2273-2278.DOI: 10.11772/j.issn.1001-9081.2014.08.2273

案例推理分类器属性权重的内省学习调整方法

张春晓,严爱军,王普

北京工业大学电子信息与控制工程学院，北京100124

收稿日期:2014-03-04 修回日期:2014-04-16 发布日期:2014-08-10 出版日期:2014-08-01
通讯作者: 严爱军
作者简介:张春晓(1983-)，女，山东日照人，博士研究生，主要研究方向：案例推理；严爱军(1970-)，男，湖北当阳人，副教授，博士，主要研究方向：人工智能、过程建模与优化控制；王普(1962-)，男，安徽合肥人，教授，博士生导师，主要研究方向：信息处理、智能控制。
基金资助:
国家自然科学基金资助项目

Introspective learning adjustment approach for attribute weights of case-based reasoning classifier

ZHANG Chunxiao,YAN Aijun,WANG Pu

College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

Received:2014-03-04 Revised:2014-04-16 Online:2014-08-10 Published:2014-08-01
Contact: YAN Aijun

摘要/Abstract

摘要：

针对案例推理（CBR）分类器中案例属性权重的分配问题，提出一种基于内省学习的属性权重迭代调整方法。该方法可根据CBR分类器对训练案例分类的结果调整属性的权重。基于成功驱动的权重学习策略，若当前训练案例分类成功，则首先根据权重调整公式增加匹配属性的权重并减少不匹配属性的权重；然后对所有权重进行归一化从而得到当次迭代的新权重。实验结果表明，所提方法的CBR分类器在UCI数据集PD、Heart和WDBC的准确率比传统CBR分类器分别提高1.72%、4.44%和1.05%。故成功驱动的内省学习权重调整方法可以提高权重分配的合理性，进而提高CBR分类器的准确率。

Abstract:

Aiming at the optimal allocation problem of attribute weights in Case-Based Reasoning (CBR) classifier, an introspective learning-based iterative adjustment approach for the attribute weights was proposed. The attribute weights could be adjusted according to the classification result of the training case by CBR classifier. Based on the success-driven weight learning strategy, if the current training case was classified successfully, the weights of matched attributes would be increased and the weights of mismatched attributes would be decreased according to weight adjustment formulas, then all of the weights would be normalized as the new weights of the current iteration. The experimental results show that the accuracy on UCI dataset PD, Heart and WDBC of CBR classifier with the proposed method are respectively 1.72%, 4.44% and 1.05% higher than the traditional CBR classifier. This illustrates that success-driven introspective learning method for the weights adjustment can improve the rationality of weight allocation, and then improve the accuracy of CBR classifier.

中图分类号:

TP18

张春晓严爱军王普. 案例推理分类器属性权重的内省学习调整方法[J]. 计算机应用, 2014, 34(8): 2273-2278.

ZHANG Chunxiao YAN Aijun WANG Pu. Introspective learning adjustment approach for attribute weights of case-based reasoning classifier[J]. Journal of Computer Applications, 2014, 34(8): 2273-2278.

参考文献

［1］de MNTARAS R L, McSHERRY D, BRIDGE D, et al. Retrieval, reuse, revision and retention in case-based reasoning ［J］. The Knowledge Engineering Review, 2005, 20(3): 215-240.
［2］SCHANK R C. Dynamic memory: a theory of reminding and learning in computers and people ［M］. Cambridge, UK: Cambridge University Press, 1982.
［3］AAMODT A, PLAZA E. Case-based reasoning: foundational is-sues, methodological variations, and system approaches ［J］. AI Communications, 1994, 7(1): 39-59.
［4］LIU W, HU G, LI J. Emergency resources demand prediction using case-based reasoning ［J］. Safety Science, 2012, 50(3): 530-534.
［5］ZHOU P, CHAI T, WANG H. Intelligent optimal-setting control for grinding circuits of mineral processing process ［J］. IEEE Transactions on Automation Science and Engineering, 2009, 6(4): 730-743.
［6］ZHAO K, YU X. A case based reasoning approach on supplier selection in petroleum enterprises ［J］. Expert Systems with Applications, 2011, 38(6): 6839-6847.
［7］LAO S I, CHOY K L, HO G T S, et al. A real-time food safety management system for receiving operations in distribution centers ［J］. Expert Systems with Applications, 2012, 39(3): 2532-2548.
［8］HSU K-H, CHIU C-C, CHIU N-H, et al. A case-based classifier for hypertension detection ［J］. Knowledge-Based Systems, 2011, 24(1): 33-39.
［9］SUBRAMANYAM R V P, SETT S K. Knowledge-based image retrieval system ［J］. Knowledge-Based Systems, 2008, 21(2): 89-100.
［10］XIAO B, ZHAO C, GUO X, et al. Skeleton-based template retrieval for virtual maize modeling ［J］. Journal of Computational Information Systems, 2010, 6(5): 1577-1582.
［11］BAX E. Validation of nearest neighbor classifiers ［J］. IEEE Transactions on Information Theory, 2012, 58(5): 3225-3234.
［12］LIU B-D, WANG Y-X, ZHANG Y-J, et al. Learning dictionary on manifolds for image classification ［J］. Pattern Recognition, 2013, 46(7): 1879-1890.
［13］RACINE K, YANG Q. Redundancy detection in semi-structured case bases ［J］. 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 ［J］. Expert Systems with Applications, 2002, 23(3): 255-264.
［15］AHN H, KIM K-J, HAN I. Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system ［J］. Expert Systems, 2006, 23(5): 290-301.
［16］LIN J-J. Pattern recognition of fabric defects using case-based reasoning ［J］.Textile Research Journal, 2010, 80(9): 794-802.
［17］KIM M, LEE S, WOO S, et al. Approximate cost estimating model for river facility construction based on case-based reasoning with genetic algorithms ［J］. KSCE Journal of Civil Engineering, 2012, 16(3): 283-292.
［18］WANG H, XU A, AI L, et al. An integrated CBR model for predicting endpoint temperature of molten steel in AOD ［J］. ISIJ International, 2012, 52(1): 80-86.
［19］WAN J, XING H, ZHANG X. Algorithm of adjusting weights of decision-makers in multi-attribute group decision-making based on entropy theory ［J］. 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［J］.控制与决策,2010,25(6):907-910.)
［20］YAN Z, LIN M, JIAN Y, et al. Entropy weight health index method of power transformer condition assessment ［C］// Proceedings of the 9th International Conference on Reliability, Maintainability and Safety. Piscataway: IEEE Press, 2011: 426-431.
［21］ONTAN S, PLAZA E. Similarity measures over refinement graphs ［J］. Machine Learning, 2012, 87(1): 57-92.
［22］SHI Z. Cognitive science ［M］. Hefei: Press of University of Science and Technology of China, 2008: 333-339.(史忠植.认知科学［M］.合肥：中国科学技术大学出版社，2008：333-339.)
［23］COX M T, RAM A. Introspective multistrategy learning: on the construction of learning strategies ［J］. Artificial Intelligence, 1999, 112(1/2): 1-55.
［24］FOX S, LEAKE D B. Learning to refine indexing by introspective reasoning ［C］// Proceedings of the First International Conference on Case-Based Reasoning. Berlin: Springer-Verlag, 1995: 431-440.
［25］BONZANO A, CUNNINGHAM P, SMYTH B. Using introspective learning to improve retrieval in CBR: a case study in air traffic control ［C］// Proceedings of the Second International Conference on Case-Based Reasoning. Berlin: Springer-Verlag, 1997: 291-302.
［26］ZHANG Z, YANG Q. Feature weight maintenance in case bases using introspective learning ［J］. Journal of Intelligent Information Systems, 2001, 16(2): 95-116.
［27］COVER T M, HART P E. Nearest neighbor pattern classification ［J］. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
［28］SALTZBURG S. A nearest hyperrectangle learning method ［J］. Machine Learning, 1991, 6(3): 251-276.
［29］FRANK A, ASUNCION A. UCI machine learning repository ［EB/OL］. ［2014-01-19］. http://archive.ics.uci.edu/ml.
［30］FAWCETT T. An introduction to ROC analysis ［J］. Pattern Recognition Letters, 2006, 27(8): 861-874.

[1]	吕乐张博瀚荆军昌刘栋. 基于持久性的多目标节点隐藏方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[2]	宫智宇王士同. 面向重尾噪声图像分类的残差网络学习方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[3]	王虎王晓峰李可马云洁. 融合多头自注意力的标签语义嵌入联邦类增量学习方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[4]	丁建立, 黄辉, 曹卫东. 航班链运行状态动态监控方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3941-3948.
[5]	刘晶鑫, 黄雯静, 徐亮胜, 黄冲, 吴建生. 字典学习与样本关联保持结合的无监督特征选择模型[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3766-3775.
[6]	宋逸飞, 柳毅. 基于数据增强和标签噪声的快速对抗训练方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3798-3807.
[7]	沈嫣然, 温昕, 张瑾昊, 张帅, 曹锐, 高保禄. 轻量级多尺度卷积网络的功能磁共振成像脑龄预测模型[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3949-3957.
[8]	张祖篡, 陈学斌, 高瑞, 邹元怀. 基于标签分类的联邦学习客户端选择方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3759-3765.
[9]	蒋权黄文清苟志勇. 基于等变图神经网络的拉格朗日粒子流模拟[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[10]	李岚皓严皓钧周号益孙庆赟李建欣. 基于神经网络的多尺度信息融合时间序列长期预测模型[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[11]	廖炎华鄢元霞潘文林. 基于YOLOv9的交通路口图像的多目标检测算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[12]	张学飞张丽萍闫盛侯敏赵宇博. 知识图谱与大语言模型协同的个性化学习推荐[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[13]	索晋贤张丽萍闫盛王东奇张雅雯. 可解释的深度知识追踪方法综述[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[14]	陈丹阳张长伦. 多尺度去相关的图卷积网络[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[15]	蒋沛宇王永光任亚亭李硕晨谭火彬. 基于测量不确定度表示指南的目标检测不确定度测量方案[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.

案例推理分类器属性权重的内省学习调整方法

Introspective learning adjustment approach for attribute weights of case-based reasoning classifier

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics