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姓名 許瑋淯(Wei-yu Hsu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 以新奇的方法有序共識群應用於群體決策問題
(A Novel Approach To Group Ranking Decision Problem By Consensus Ordered Segments)
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摘要(中) 群體決策問題已經越來越被關注且被認為是一個重要的研究領域,由於此研究領域已經被廣泛的應用。為了解決群體決策問題,傳統的解決方法分別提出兩種輸出格式:產生一個完整且有序的序列和許多的共識序列。但是,不管哪一種輸出格式,針對群體決策問題仍然存在一些缺點。因此,我們定義了有序共識群來當作一種新的輸出格式。此研究主要結合分群的方法且採用有序的概念來說服先前研究的缺點。之後我們提出一個演算法,從使用者的序列資列中來發掘有序共識群。最後,關於實驗的部份,我們使用真實與合成資料集來表示有序共識群的可用性。
摘要(英) The group ranking problem has received more and more attention due to its widespread applications. Traditional solution either produces an ordering list of all items or many consensus sequences as output. Unfortunately, no matter which output is generated, some weaknesses exist in the group ranking problem. Accordingly, this research combines the clustering methods with the ordering concepts to address the weaknesses of previous research, and define the consensus ordered segments as a new type of output of the group ranking problem. We also proposed an algorithm to mine consensus ordered segments from users’ ranking data. Finally, extensive experiments have been carried out using the real and synthetic dataset to demonstrate the usefulness of consensus ordered segments.
關鍵字(中) ★ 群體決策
★ 有序共識群
★ 資料挖掘
關鍵字(英) ★ Consensus ordered segments
★ Data mining
★ Group decision making
論文目次 摘要 i
Abstract ii
誌 謝 iii
Contents iv
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research Objectives 4
1.4 Thesis Framework 7
Chapter 2 Related Works 8
2.1 Group ranking problem 8
2.2 Our work 11
Chapter 3 Problem Definition 13
3.1 Research Problem 13
3.2 Definitions 14
Chapter 4 Algorithm 22
4.1 Overview of the algorithm 22
4.2 Mining consensus ordered segments 24
4.2.1 Procedure of the algorithm 24
4.2.2 Procedure of the movement 32
Chapter 5 Experiment 36
5.1 Data collections 36
5.2 Performance evaluation 37
5.2.1 Run time comparison 37
5.2.2 Run time comparison 42
5.2.3 Real data set implementation 45
Chapter 6 Conclusion 52
6.1 Contributions 52
6.2 Future works 52
References 54
參考文獻 [1] W.D. Cook, B. Golany, M. Kress, M. Penn, T. Raviv, “Optimal allocation of proposals to reviewers to facilitate effective ranking,” Management Science (51:4), 2005, pp. 655–661.
[2] E. Fernandez, R. Olemdo. “An agent model based on ideas of concordance and discordance for group ranking problems,” Decision Support Systems (39:3), 2005, pp. 429–443.
[3] Y.L. Chen, L.C. Cheng. “A novel collaborative filtering approach for recommending ranked items,” Expert Systems with Applications (34:4), 2008, pp. 2396–2405.
[4] R. Fagin, R. Kumar, D. Sivakumar. “Efficient similarity search and classification via rank aggregation,” Proceedings of the ACM SIGMOD international conference on management of data, ACM, San Diego, California, 2003, pp. 301–312.
[5] M.M.S. Beg, N. Ahmad. “Soft computing techniques for rank aggregation on the World Wide Web,” World Wide Web-Internet and Web Information Systems (6:1), 2003, pp. 5–22.
[6] W. W. Cohen, R. E. Schapire and Y. Singer. Learning to order things. J. of Artificial Intelligence research 10, 1999, pp. 243-270.
[7] B. Golden, The analytic hierarchy process: applications and studies, Springer, New York NY, 1989.
[8] J.G. Kemeny, L.J. Snell, “Preference ranking: an axiomatic approach,” Proceedings of mathematical models in the social science, 1962, pp. 9–23.
[9] M. Kendall, Rank correlation methods, ThirdHafner, New York, 1955.
[10] F.D. Robert, H.F. Ernest. “Group decision support with the analytic hierarchy process,” Decision Support System (8:2), 1992, pp. 99–124.
[11] T.L. Saaty. “Rank generation, preservation, and reversal in the analytic hierarchy decision process,” Decision Sciences (18:2), 1987, pp. 157–177.
[12] K. Bogart. “Preference structures I: distances between transitive preference relations,” Journal of Math Sociology, 1973, pp. 49–67.
[13] K. Bogart, “Preference structures II: distances between asymmetric relations,” SIAM Journal of Applied Math (29:2), 1975, pp. 254–265.
[14] W.D. Cook, M. Kress, L. Seiford. “An axiomatic approach to distance on partial orders,” Revue Automatique, Informatique et Recherche Operationnelle (20:2), 1986, pp. 115–122.
[15] W.D. Cook, M. Kress, L. Seiford, “Information and preference in partial orders: a bimatrix representation,” Psychometrika (51:2), 1986, pp. 197–207.
[16] W.D. Cook, M. Kress, L. Seiford. “A general framework for distance-based consensus in ordinal ranking models,” European Journal of Operational Research, 1996, pp. 392–397.
[17] S. Greco, V. Mousseau, R. Slowinski. “Ordinal regression revisited: multiple criteria ranking using a set of additive value functions,” European Journal of Operational Research (191:2), 2008, pp. 416–436.
[18] D.S. Hochbaum, A. Levin. “Methodologies and algorithms for group-rankings decision,” Management Science (52:9), 2006, pp. 1394–1408.
[19] W.D. Cook, B. Golany, M. Kress, M. Penn, T. Raviv. “Optimal allocation of proposals to reviewers to facilitate effective ranking,” Management Science (51:4), 2005, pp. 655–661.
[20] W.D. Cook, B. Golany, M. Kress, M. Penn, T. Raviv. “Creating a consensus ranking of proposals from reviewer’s partial ordinal rankings,” Computers and OR (34:4), 2007, pp. 954–965.
[21] J. Gonzalez-Pachon, C. Romero. “Aggregation of partial ordinal rankings: An interval goal programming approach,” Computers and Operations Research (28:8), 2001, pp. 827–834.
[22] D.S. Hochbaum, A. Levin. “Methodologies and algorithms for group-rankings decision,” Management Science (52:9), 2006, pp. 1394–1408.
[23] Y.L. Chen, L.C. Cheng. “Mining maximum consensus sequences from group ranking data,” European Journal of Operational Research (198:1), 2009, pp. 241–251.
[24] J.C. Borda, Memoire sur les elections au scrutin, Histoire de l'Academie Royale de Science, Paris, 1981.
[25] J. Bartholdi, C.A. Tovey, M.A. Trick. “Voting schemes for which it can be difficult to tell who won the election,” Social Choice and Welfare (6:2), 1989, pp. 157–165.
[26] T. Saaty. “A scaling method for priorities in hierarchical structures,” Journal of Mathematical Psychology (15:3), 1977, pp. 234–281.
[27] T. Saaty. The Analytic Hierarchy Process, McGraw-Hill, New York, 1980.
[28] R. Fagin, R. Kumar, D. Sivakumar. “Efficient similarity search and classification via rank aggregation,” Proceedings of the ACM SIGMOD international conference on management of data, ACM, San Diego, California, 2003, pp. 301–312.
[29] S. Damart, L.C. Dias, V. Mousseau. “Supporting groups in sorting decisions: methodology and use of a multi-criteria aggregation/disaggregation DSS,” Decision Support Systems (43:4), 2007, pp. 1464–1475.
[30] W.D. Cook, B. Golany, M. Kress, M. Penn, T. Raviv. “Creating a consensus ranking of proposals from reviewer's partial ordinal rankings,” Computers & Operations Research (34:4), 2007, pp. 954–965.
指導教授 陳彥良(Yen-liang Chen) 審核日期 2011-7-11
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