含非连续性信息多属性案例中的决策知识发现方法

Abstract

Abstract: The information with non-continuous features is ubiquitous in diagnosis and treatment decision making cases. The knowledge acquisition of the cases with this kind of feature has always been a key and bottleneck in multi-attribute case decision making. In this paper, conditional probability and GAs are integrated into case-based reasoning technology to develop an extension method of traditional KNN——CRMGACP algorithm, which includes a GAs-based weight determination method and an improved similarity algorithm integrating the conditional probability. Collecting data from AH Hospital, which is one of large-scale hospitals in Anhui province, CancerCBRSys is developed as the experimental tool for tests. Experimental study is competed by comparing the performance amongst four different case-based reasoning methods. The results show that CRMGACP has the best performance and shows significant advantage in various statistics. In general, CRM-GACP solves the problem of knowledge discovery from non-continuous cases and is hopeful to be a powerful decision-making tool in the research area of clinical decision making.

Key words: complex multi-attribute decision making, public healch mangement, knowledge discovery, discrete variable, case-based reasoning

CLC Number:

C931.6

LIANG Chang-yong, GU Dong-xiao, CHENG Wen-juan, YANG Chang-hui, GU Zuo-zuo. A Decision Knowledge Discovery Method for Multi-attribute Cases with Non-continuous Features[J]. Chinese Journal of Management Science, 2014, 22(4): 83-91.

References

[1] Ayer T, Chhatwal J, Alagoz O, et al. Informatics in radiology: Comparison of logistic regression and artificial neural network models in breast cancer risk estimation[J]. Radio Graphics, 2010, 30(1): 13-22.

[2] Hung M S, Shanker M, Hu M Y. Estimating breast cancer risks using neural networks[J]. Journal of the Operational Research Society, 2002, 53(2):222-231.

[3] Lieber J, Bresson B. Case-based reasoning for breast cancer treatment decision helping[C]. Blanzieri E, Portinale L. Advances in Case-Based Reasoning-Proceedings of the fifth European Workshop on Case-Based Reasoning (EWCBR-2k), Berlin: Springer, 2000.

[4] Floyd Jr C E, Lo J Y, Tourassi G D. Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions[J]. American Iournal of Roentgenol, 2000, 175(5):1347-1352

[5] Bilska A O, Floyd Jr C E. Investigating different similarity measures for a case-based reasoning classifier to predict breast cancer[J]. Imaging, 2001, 4322:1862-1866.

[3] 赵卫东, 盛昭瀚. 基于案例推理的决策问题求解研究[J].管理科学学报, 2000, 3(4): 29-36.

[4] 梁昌勇, 顾东晓. 面向不确定多属性决策问题的范例检索算法研究[J]. 中国管理科学, 2008, 17(1):131-137.

[5] 路云, 吴应宇, 达庆利.基于案例推理技术的企业经营决策支持模型设计术[J].中国管理科学, 2005, 13(2): 81-87.

[6] Gu Dongxiao, Liang Changyong, Li Xingguo, et al. Intelligent technique for knowledge reuse of dental medical records based on case-based reasoning[J]. Journal of medical systems, 2010, 34(2): 213-222.

[7] McCane B, Albert M. Distance functions for categorical and mixed variables[J]. Pattern Recognition Letters, 2008, 29(7): 986-993.

[8] Stanfill C, Waltz D L. Toward memory-based reasoning[J]. Communication of the ACM, 1986, 29(12): 1213-1228.

[9] Wilson D, Martinez T. Improved heterogeneous distance functions[J]. Journel of Artificial Intelligence Research, 1997, 6:1-34.

[10] Gower J C. A general coefficient of similarity and some of its properties[J]. Biometrics, 1971, 27: 857-874.

[11] Greene D, Freyne J, Smyth B. Padraig cunningham: An analysis of research themes in the CBR conference literature. ECCBR, 2008, 5239: 18-43.

[12] Renauda J, Levratb E, Fonteixc C. Weights determination of OWA operators by parametric identification[J]. Mathematics and Computers in Simulation, 2008, 77: 499-511

[13] Zhang Lu, Coenen F, Leng P. Formalising optimal feature weight setting in case-based diagnosis as linear programming problems[J]. Knowledge-Based Systems, 2002, 15(7): 391-398.

[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] Dogan S Z, Arditi D, Gnaydin H M. Using decision trees for determining attribute weights in a case-based model of early cost prediction[J]. Journal of Construction Engineering and Management, 2008, 134 (2): 146-152.

[16] Fidelis M V, Lopes H S, Freitas A A. Discovering comprehensible classification rules with a genetic algorithm[C]. Proceeding of the 2000 Congress on Evolutionary Computation, La Jolla, July 16-19, 2000.

[17] Goldberg D E. Genetic algorithms in search, optimization and machine learning[J]. Machine Learning, 1988, 3(2-3):95-99.

[18] Cunningham P. A taxonomy of similarity mechanisms for case-based reasoning[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 21(11):1532-1543.

[19] West D, Mangiameli P, Rampal R, et al. Ensemble strategies for a medical diagnosis previous termdecisionnext term support system: A breast previous term cancer next term diagnosis application[J]. Europeon Journal of Operatinal Research, 2005, 162(2): 532-551.

[20] Lee S. Using data envelopment analysis and decision trees for efficiency analysis and recommendation of B2C controls[J]. Decision Support Systems, 2010, 49(4): 486-497.

[21] Salchenberger L M, Cinar E M, Lash N A. Neural networks: A new tool for predicting thrift failures[J]. Decision Sciences, 1992, 23(4): 899-916.

[22] Karabatak M, Cevdet Ince M. An expert system for detection of breast cancer based on association rules and neural network[J]. Expert Systems with Applications, 2009, 36(2): 3465-3469.

[23] Sexton R S, Dorsey R E. Reliable classification using neural networks: A genetic algorithm and back propagation comparison[J]. Decision Support Systems, 2000, 30(1): 11-22.

[24] Zhang Zhen, Zhang Hong, Bast Jr R C. An application of artificial neural networks in ovarian cancer early detection[C]. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, July 24-27, 2000.

[25] Tan T Z, Quek C, See Ng G, et al. Ovarian cancer diagnosis with complementary learning fuzzy neural network[J]. Artificial Intelligence in Medicine, 2008, 43(3): 207-222.

[26] Chen Yuehui, Wang Yan, Yang Bo. Evolving hierarchical RBF neural networks for breast cancer detection[J]. ICONIP, 2006, (3):137-144.

[27] Cruz-Ramirez N, Acosta-Mesa H G, Carrillo-Calvet H, et al. Discovering interobserver variability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks[J]. Applied Soft Computing, 2009, 9(4): 1331-1342.

[28] Antal P, Verrelst H, Timmerman D, et al. Bayesian networks in ovarian cancer diagnosis: Potentials and limitations[C]. Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00), Houston, June 23-24, 2000.

[29] Chang C L, Hsu Mingyuan. The study that applies artificial intelligence and logistic regression for assistance in differential diagnostic of pancreatic cancer[J]. Expert Systems with Applications, 2009, 36(7): 10663-10672.

[30] Witten I H, Frank E. Data mining: Practical machine learning tools and techniques[M]. Morgan Kaufmann: Elsevier, 2005.

[31] Venkatesh V, Speier C, Morris M G. User acceptance enablers in individual decision making about technology: Toward an integrated model[J]. Decision Sciences, 2002, 33(2): 297-316.

[32] Wixom B H, Todd P A. A theoretical integration of user satisfaction and technology acceptance[J]. Information Systems Research, 2005, 16(1): 85-102.

[33] Venkatesh V, Morris M G. Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior[J]. MIS Quarterly, 2000, 24(1): 115-139.

[34] 陈浪涛, 张成洪, 张诚. 协同商务环境下基于案例推理机制研究[J]. 复旦学报(自然科学版), 2005, 44(6): 1009-1015.

A Decision Knowledge Discovery Method for Multi-attribute Cases with Non-continuous Features

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 5

Metrics

Comments

Recommended 0

[1]	WU Shuang-sheng, LIN Jie, HUANG Dong-hong, ZHANG Zhen-yu. Similar Cases Extraction and Amount Estimation of Person Subjected to Execution Concealing Property Based on Similarity of Heterogeneous Information [J]. Chinese Journal of Management Science, 2023, 31(6): 221-230.
[2]	LI Peng, ZHU Jian-jun, LIU Si-feng. Intuitionistic Fuzzy Decision-making Methods Based on Case-based Reasoning [J]. Chinese Journal of Management Science, 2015, 23(7): 113-118.
[3]	HE Chao-lin. Optimal Portfolio Choice under the Mean-Variance Model with General Uncertainty [J]. Chinese Journal of Management Science, 2015, 23(12): 63-70.
[4]	LU Yun, WU Ying-yu, DA Qing-li. A Decision Support Model of Enterprise Based on CBR Technique [J]. Chinese Journal of Management Science, 2005, (2): 81-87.
[5]	ZHANG Zhe, CHANG Gui-ran, HUANG Xiao-yuan. The Application of Data Mining Technology in CRM [J]. Chinese Journal of Management Science, 2003, (1): 53-59.