Robust reductions from ranking to classification

Machine Learning

Volumn 72, Issue 1-2, 2008, Pages 139-153

  Balcan, Maria Florina ^a   Bansal, Nikhil ^b   Beygelzimer, Alina ^b   Coppersmith, Don ^c   Langford, John ^d   Sorkin, Gregory B ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c CENTER FOR COMMUNICATIONS RESEARCH   (United States)

^d YAHOO RESEARCH   (United States)

Author keywords

Classification; Ranking; Reductions

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); REDUCTION;

BINARY CLASSIFICATION; BINARY PROBLEMS; RANKING; RECEIVER OPERATING CHARACTERISTIC CURVES;

SOFTWARE ENGINEERING;

EID: 84859817220     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5058-6     Document Type: Article

References (15)

1. Agarwal, S., & Niyogi, P. (2005). Stability and generalization of bipartite ranking algorithms. In Proceedings of the eighteenth annual conference on computational learning theory (COLT) (pp. 32-47).
2. Agarwal, S., Har-Peled, S., & Roth, D. (2005). A uniform convergence bound for the area under the ROC curve. In Proceedings of the 10th international workshop on artificial intelligence and statistics.
3. Ailon, N., & Mohri, M. (2007). An efficient reduction of ranking to classification (Technical Report TR-2007-903). New York University.
4. Alon, N. (2006). Ranking tournaments. SIAM Journal on Discrete Mathematics, 20, 137-142.
5. Bansal, N., Coppersmith, D., & Sorkin, G. B. (2006). A winner-loser labeled tournament has at most twice as many outdegree misrankings as pair misrankings (IBM Research Report RC24107). November 2006.
6. Clémençon, S., Lugosi, G., & Vayatis, N. (2005). Ranking and scoring using empirical risk minimization. In Proceedings of the eighteenth annual conference on computational learning theory (COLT) (pp. 1-15).
7. Cohen, W., Schapire, R., & Singer, Y. (1999). Learning to order things. Journal of Artificial Intelligence Research, 10, 243-270.
8. Coppersmith, D., Fleischer, L., & Rudra, A. (2006). Ordering by weighted number of wins gives a good ranking for weighted tournaments. In Proceeding of the 17th annual symposium on discrete algorithms (SODA) (pp. 776-782).
9. Cortes, C., & Mohri, M. (2004). AUC optimization versus error rate minimization. In Advances in neural information processing systems (NIPS).
10. Freund, Y., Iyer, R., Schapire, R., & Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933-969.
11. Landau, H. (1953). On dominance relations and the structure of animal societies, III: the condition for a score structure. Bulletin of Mathematical Biophysics, 15, 143-148.
12. Langford, J., & Zadrozny, B. (2005). Estimating class membership probabilities using classifier learners. In Proceedings of the 10th international workshop on artificial intelligence and statistics.
13. Marshall, A., & Olkin, I. (1979). Mathematics in science and engineering: Vol. 143. Inequalities: theory of majorization and its applications. New York.
14. Rudin, C., Cortes, C., Mohri, M., & Schapire, R. (2005). Margin-based ranking meets boosting in the middle. In Proceedings of the eighteenth annual conference on computational learning theory (COLT).
15. Slater, P. (1961). Inconsistencies in a schedule of paired comparisons. Biometrika, 48, 303-312.