Cost-sensitive learning based on bregman divergences

Machine Learning

Volumn 76, Issue 2-3, 2009, Pages 271-285

  Santos Rodríguez, Raúl ^a   Guerrero Curieses, Alicia ^b   Alaiz Rodríguez, Rocío ^c   Cid Sueiro, Jesús ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b UNIVERSIDAD REY JUAN CARLOS   (Spain)

^c UNIVERSITY OF LEÓN   (Spain)

Author keywords

Bregman divergence; Cost sensitive learning; Maximum margin; Posterior class probabilities

Indexed keywords

BREGMAN DIVERGENCE; BREGMAN DIVERGENCES; COST SENSITIVE LEARNING; DECISION BOUNDARY; DECISION COST; DESIGN COSTS; DIVERGENCE MEASURES; MAXIMUM MARGIN; MULTI-CLASS PROBLEMS; POSTERIOR CLASS PROBABILITIES; POSTERIOR PROBABILITY;

CLASSIFIERS; COSTS; LEARNING SYSTEMS;

PROBABILITY;

EID: 68949096588     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-009-5132-8     Document Type: Conference Paper

References (27)

1. Abe, N., Zadrozny, B., & Langford, J. (2004). An iterative method for multi-class cost-sensitive learning. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 3-11). New York: ACM.
2. A. Banerjee X. Guo H. Wang 2005 On the optimality of conditional expectation as a Bregman predictor IEEE Transactions on Information Theory 51 7 2664 2669
3. Bradford, J. P., Kunz, C., Kohavi, R., Brunk, C., & Brodley, C. E. (1998). Pruning decision trees with misclassification costs. In Proceedings of the European conference on machine learning (pp. 131-136). Berlin: Springer.
4. L. M. Bregman 1967 The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming USSR Computational Mathematics and Mathematical Physics 7 10 200 217
5. Cid-Sueiro, J., & Figueiras-Vidal, A. R. (2001). On the structure of strict sense Bayesian cost functions and its applications. IEEE Transactions on Neural Networks, 12(3).
6. J. Cid-Sueiro J. I. Arribas S. Urbán-Muñoz A. R. Figueiras-Vidal 1999 Cost functions to estimate a posteriori probabilities in multi-class problems IEEE Transactions on Neural Networks 10 3 645 656
7. I. S. Dhillon A. Banerjee S. Merugu J. Ghosh 2005 Clustering with Bregman divergences Journal of Machine Learning Research 6 1705 1749
8. Fan, W., Stolfo, S. J., Zhang, J., & Chan, P. K. (1999). Adacost: misclassification cost-sensitive boosting. In Proc. 16th international conf. on machine learning (pp. 97-105). San Mateo: Morgan Kaufmann.
9. T. Gneiting A. E. Raftery 2007 Strictly proper scoring rules, prediction, and estimation Journal of the American Statistical Association 102 477 359 378
10. A. Guerrero-Curieses J. Cid-Sueiro R. Alaiz-Rodríguez A. Figueiras 2004 Local estimation of posterior class probabilities to minimize classification errors IEEE Transactions on Neural Networks 15 2 309 317
11. A. Guerrero-Curieses R. Alaiz-Rodríguez J. Cid-Sueiro 2005 Loss function to combine learning and decision in multiclass problems Neurocomputing 69 3 17
12. Kapur, J. N., & Kesavan, H. K. (1993). Entropy optimization principles with applications. San Diego: Academic Press.
13. Kukar, M. Z., & Kononenko, I. (1998). Cost-sensitive learning with neural networks. In Proceedings of the 13th European conference on artificial intelligence (ECAI-98) (pp. 445-449). New York: Wiley.
14. X. Y. Liu Z. H. Zhou 2006 Training cost-sensitive neural networks with methods addressing the class imbalance problem IEEE Transactions on Knowledge and Data Engineering 18 1 63 77
15. Lozano, A. C., & Abe, N. (2008). Multi-class cost-sensitive boosting with p-norm loss functions. In KDD '08: proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 506-514). New York: ACM.
16. Marrocco, C., & Tortorella, F. (2004). A cost-sensitive paradigm for multiclass to binary decomposition schemes. Lecture notes in computer science (Vol. 3138, pp. 753-761). Berlin: Springer.
17. J. W. Miller R. Goodman P. Smyth 1993 On loss functions which minimize to conditional expected values and posterior probabilities IEEE Transactions on Information Theory 39 4 1404 1408
18. O'Brien, D. B., & Gray, R. M. (2005). Improving classification performance by exploring the role of cost matrices in partitioning the estimated class probability space. In Proceedings of the ICML workshop on ROC analysis (pp. 79-86).
19. O'Brien, D. B., Gupta, M. R., & Gray, R. M. (2008). Cost-sensitive multi-class classification from probability estimates. In ICML '08: proceedings of the 25th international conference on machine learning (pp. 712-719). New York: ACM.
20. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers (pp. 61-74). Cambridge: MIT Press.
21. F. Provost T. Fawcett 2001 Robust classification systems for imprecise environments Machine Learning 42 3 203 231
22. Savage, L. J. (1971). Elicitation of personal probabilities and expectations. Journal of the American Statistical Association (pp. 783-801).
23. Stuetzle, W., Buja, A., & Shen, Y. (2005). Loss functions for binary class probability estimation and classification: Structure and applications (Technical report). Department of Statistics, University of Pennsylvania.
24. Zadrozny, B., & Elkan, C. (2001a). Learning and making decisions when costs and probabilities are both unknown. In Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 204-213). New York: ACM.
25. Zadrozny, B., & Elkan, C. (2001b). Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In ICML '01: proceedings of the eighteenth international conference on machine learning (pp. 609-616). San Francisco: Morgan Kaufmann.
26. Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. In KDD '02: proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 694-699). New York: ACM.
27. Zadrozny, B., Langford, J., & Abe, N. (2003). Cost-sensitive learning by cost-proportionate example weighting. In ICDM '03: proc. of the 3rd IEEE int. conf. on data mining (p. 435). Washington: IEEE Comput. Soc.