Heterogeneous Evidence Chains Based Fusion Reasoning for Multi-attribute Group Decision Making
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摘要: 针对多属性群决策中可解释性证据融合推理的实体异构性问题,给出了一个实体异构性下证据链融合推理的多属性群决策方法.基于证据推理理论,引入证据链关联的概念,从多数据表提供的数据矩阵中获取可区分的近邻证据集,推导了各数据表的相似度矩阵,并构建半正定矩阵的二次优化模型,共享群决策专家的经验知识.使用Dempster正交规则,论证了异构实体之间可解释性推理中可信度融合的合理性,并使用证据融合规则集成各个数据表的近邻证据中获得的可信度,验证了调和多源异构数据中不一致信息的有效性.通过具有实体异构性的心脏病多决策数据诊断实例说明了方法的可行性与合理性.Abstract: In multi-attribute group decision making, the heterogeneity of entities causes a lot difficulties for the interpretable evidence fusion reasoning process, thus a novel heterogeneous evidential chains based fusion reasoning(Hefur) method is proposed for multi-attribute group decision making. Based on the theory of evidential reasoning, the concept of evidential chain association is introduced to obtain the nearest neighbor set of distinct evidences from the data matrix of multiple decision tables. Similarity matrices are derived from data tables, and positive semi-definite matrix quadratic optimization model is built to share, sharing the experience knowledge of the group decision-making experts. Using the Dempster's quadrature rule, the rationality of the belief integrating is verified in the interpretable reasoning process with heterogeneous entities, and the combined belief is obtained from nearest neighbor evidences for each data table using the evidence fusion rules. Moreover, the validity is verified for dealing with the harmonic information inconsistence of the multi-heterogeneous data sources. Numerical experiments on the heart disease diagnosis with entity heterogeneity illustrate the feasibility and rationality of the proposed method.
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[1] Scott D, Lee J, Silva I, Park S, Moody G, Celi L, Mark R G. Accessing the public MIMIC-Ⅱ intensive care relational database for clinical research. BMC Medical Informatics and Decision Making, 2013, 13:9 [2] [2] Scott M, Boardman R P, Reed P A, Cox S J. Managing heterogeneous datasets. Information Systems, 2014, 44:34-53 [3] [3] Hoffmann S, Fischbeck P, Krupnick A, McWilliams M. Elicitation from Large, Heterogeneous expert panels:using multiple uncertainty measures to characterize information quality for decision analysis. Decision Analysis, 2007, 4(2):91-109 [4] [4] Krishnan R, Li X P, Steier D, Zhao L. On heterogeneous database retrieval:a cognitively guided approach. Information Systems Research, 2001, 12(3):286-301 [5] [5] Baron J, Mellers B A, Tetlock P E, Stone E, Ungar L. Two reasons to make aggregated probability forecasts more extreme. Decision Analysis, 2014, 11(2):133-145 [6] [6] O'Leary D E. Artificial intelligence and big data. IEEE Intelligent Systems, 2013, 28(2):96-99 [7] [7] Fan J Q, Han F, Liu H. Challenges of big data analysis. National Science Review, 2014, 12(1):293-314 [8] [8] Mehenni T, Moussaoui A. Data mining from multiple heterogeneous relational databases using decision tree classification. Pattern Recognition Letters, 2012, 33(13):1768-1775 [9] [9] Manjunath G, Narasimha Murty M, Sitaram D. Combining heterogeneous classifiers for relational databases. Pattern Recognition, 2013, 46(1):317-324 [10] Yang Yi, Han De-Qiang, Han Chong-Zhao. Evidence combination based on multi-criteria rank-level fusion. Acta Automatica Sinica, 2012, 38(5):823-831(杨艺, 韩德强, 韩崇昭. 基于多准则排序融合的证据组合方法. 自动化学报, 2012, 38(5):823-831) [11] Hu Chang-Hua, Si Xiao-Sheng, Zhou Zhi-Jie, Wang Peng. An improved D-S algorithm under the new measure criteria of evidence conflict. Acta Electronica Sinica, 2009, 37(7):1578-1583(胡昌华, 司小胜, 周志杰, 王鹏. 新的证据冲突衡量标准下的D-S改进算法. 电子学报, 2009, 37(7):1578-1583) [12] Dey D, Sarkar S, De P. A probabilistic decision model for entity matching in heterogeneous databases. Management Science, 1998, 44(10):1379-1395 [13] Billot A, Gilboa I, Schmeidler D, Samet D. Probabilities as similarity-weighted frequencies. Econometrica, 2005, 73(4):1125-1136 [14] Wang F, Sun J, Ebadollahi S. Composite distance metric integration by leveraging multiple experts' inputs and its application in patient similarity assessment. Statistical Analysis and Data Mining, 2012, 5(1):54-69 [15] Segev A, Zhao J L. Rule management in expert database systems. Management Science, 1994, 40(6):685-707 [16] Yang J B, Liu J, Wang J, Sii H, Wang H. Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Transactions on Systems, Man, and Cybernetics, Part A:Systems and Humans, 2006, 36(2):266-285 [17] Wang J Q, Zhang H Y. Multicriteria decision-making approach based on atanassov's intuitionistic fuzzy sets with incomplete certain information on weights. IEEE Transactions on Fuzzy Systems, 2013, 21(3):510-515 [18] Wang J Q, Nie R R, Zhang H Y, Chen X H. Intuitionistic fuzzy multi-criteria decision-making method based on evidential reasoning. Applied Soft Computing, 2013, 13(4):1823-1831 [19] Yang J B, Xu D L. Evidential reasoning rule for evidence combination. Artificial Intelligence, 2013, 205:1-29 [20] Zhao H, Ram S. Combining schema and instance information for integrating heterogeneous data sources. Data Knowledge Engineering, 2007, 61(2):281-303 [21] Xu M, Yu H Y, Shen J. New algorithm for CBR-RBR fusion with robust thresholds. Chinese Journal of Mechanical Engineering, 2012, 25(6):1255-1263 [22] Li Xin-De, Wang Feng-Yu. A method of evidence reasoning based on the ISODATA clustering and improved similarity Measure, Acta Automatica Sinica, 2015, 41(3):575-590(李新德, 王丰羽. 一种基于ISODATA聚类和改进相似度的证据推理方法, 自动化学报, 2015, 41(3):575-590) [23] Tian Zhi-Hong, Yu Xiang-Zhan, Zhang Hong-Li, Fang Bin-Xing. A real-time network intrusion forensics method based on evidence reasoning network. Chinese Journal of Computers, 2014, 37(5):1184-1194(田志宏, 余翔湛, 张宏莉, 方滨兴. 基于证据推理网络的实时网络入侵取证方法. 计算机学报, 2014, 37(5):1184-1194) [24] Reshef D N, Reshef Y A, Finucane H K, Grossman S R, McVean G, Turnbaugh P J, Lander E S, Mitzenmacher M, Sabeti P C. Detecting novel associations in large data sets. Science, 2011, 334(6062):1518-1524 [25] Bordley R F. Using Bayes' rule to update an event's probabilities based on the outcomes of partially similar events. Decision Analysis, 2011, 8(2):117-127 [26] Jiang Z R, Sarkar S, De P, Dey D. A framework for reconciling attribute values from multiple data sources. Management Science, 2007, 53(12):1946-1963 [27] Xu M, Yu H-Y, Shen J. New approach to eliminate structural redundancy in case resource pools using alpha mutual information. Journal of Systems Engineering and Electronics, 2013, 24(4):625-633 [28] Yang Jin-Feng, Yu Qiu-Bin, Guan Yi, Jiang Zhi-Peng. An overview of research on electronic medical record oriented named entity recognition and entity relation extraction. Acta Automatica Sinica, 2014, 40(8):1537-1562(杨锦锋, 于秋滨, 关毅, 蒋志鹏. 电子病历命名实体识别和实体关系抽取研究综述. 自动化学报, 2014, 40(8):1537-1562) [29] Basir O, Yuan X H. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Information Fusion, 2007, 8(4):379-386 [30] Wong S K M, Lingras P. Representation of qualitative user preference by quantitative belief functions. IEEE Transactions on Knowledge and Data Engineering, 1994, 6(1):72-78 [31] Xue Yong-Jian, Ni Zhi-Wei. Research of large scale manifold learning based on MapReduce. Systems Engineering Theory Practice, 2014, 34(S1):151-157(薛永坚, 倪志伟. 基于MapReduce的大规模数据集流形学习降维研究. 系统工程理论实践, 2014, 34(S1):151-157)
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