A probabilistic framework for the design of instance-based supervised ranking algorithms in an ordinal setting

Annals of Operations Research

Volumn 163, Issue 1, 2008, Pages 115-142

  Lievens, S ^a   DeBaets B ^a   Cao Van, K ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Instance based learning; Monotone classification; Stochastic dominance; Supervised ranking

Indexed keywords

EID: 49249099254     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-008-0326-1     Document Type: Article

References (27)

1. D. W. Aha D. Kibler M. K. Albert 1991 Instance-based learning algorithms Machine Learning 6 1 37 66
2. A. Ben-David 1992 Automatic generation of symbolic multiattribute ordinal knowledge-based DSSs: Methodology and applications Decision Sciences 23 6 1357 1372
3. A. Ben-David 1995 Monotonicity maintenance in information-theoretic machine learning algorithms Machine Learning 19 1 29 43
4. A. Ben-David L. Sterling Y. H. Pao 1989 Learning and classification of monotonic ordinal concepts Computational Intelligence 5 45 49
5. Bioch, J., & Popova, V. (2000). Rough sets and ordinal classification. In A. Arimura & S. Jain (Eds.), Algorithmic learning theory. Notes in artificial intelligence, 1968 (pp. 291-305). New York: Springer.
6. Bioch, J., & Popova, V. (2002). Monotone decision trees and noisy data. In H. Blockeel & M. Denecker (Eds.), Proceedings of the 14th Belgian-Dutch conference on machine learning (BNAICC'2002) (pp. 19-26).
7. Bouyssou, D. (1990). Building criteria: a prerequisite for MCDA. In C. Bana e Costa (Ed.), Readings in multiple criteria decision aid (pp. 58-80). Heidelberg: Springer.
8. Cao-Van, K. (2003). Supervised ranking: from semantics to algorithms. PhD thesis, Ghent University.
9. K. Cao-Van B. De Baets 2002 On the definition and representation of a ranking Lecture Notes in Computer Science 2561 291 299
10. K. Cao-Van B. De Baets 2003 Consistent representation of rankings Lecture Notes in Computer Science 2929 107 123
11. Compact Oxford English dictionary of current English (2005). Oxford University Press.
12. Fishburn, P. C. (1985). Interval orders and interval graphs. New York: Wiley.
13. E. Frank M. Hall 2001 A simple approach to ordinal classification Lecture Notes in Computer Science 2167 145 156
14. G. Gediga I. Düntsch 2002 Approximation quality for sorting rules Computational Statistics & Data Analysis 40 3 499 526
15. S. Greco B. Matarazzo R. Slowinski 2002 Rough approximation by dominance relations International Journal of Intelligent Systems 17 2 153 171
16. S. Greco R. Slowinski J. Stefanowski M. Zurawski 2004 Incremental versus non-incremental rule induction for multicriteria classification Lecture Notes in Computer Science 3135 33 53
17. Levy, H. (1998). Stochastic dominance. Boston: Kluwer Academic.
18. Makino, K., Suda, T., Yano, K., & Ibaraki, T. (1996). Data analysis by positive decision trees. In Proc. international symposium on cooperative database systems for advanced applications (pp. 282-289).
19. K. Makino T. Suda H. Ono T. Ibaraki 1999 Data analysis by positive decision trees IEICE Transactions on Information and Systems E82-D 1 76 88
20. J. L. Marichal P. Meyer M. Roubens 2005 Sorting multi-attribute alternatives: the TOMASO method Computers and Operations Research 32 861 877
21. W. Ogryczak A. Ruszczyński 2002 Dual stochastic dominance and related mean-risk models SIAM Journal of Optimization 13 1 60 78
22. Potharst, R., & Bioch, J. C. (1998). Quasi-monotone decision trees for ordinal classification. In F. Verdenius & W. van den Broek (Eds.), Proceedings of the 8th Belgian-Dutch conference on machine learning (BENELEARN'98) (pp. 122-131).
23. R. Potharst J. C. Bioch 2000 Decision trees for ordinal classification Intelligent Data Analysis 4 97 111
24. R. Potharst A. J. Feelders 2002 Classification trees for problems with monotonicity constraints SIGKDD Explorations 4 1 1 10
25. J. R. Quinlan 1986 Induction of decision trees Machine Learning 1 81 106
26. A. Verkeyn D. Botteldooren B. De Baets G. De Tré 2003 Sugeno integrals for the modelling of noise annoyance aggregation Lecture Notes in Artificial Intelligence 2715 277 284
27. Witten, I. H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques with Java implementations (2nd edn.). San Mateo: Morgann Kaufmann.