Asymmetric boosting

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 609-619

  Masnadi Shirazi, Hamed ^a   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; COST EFFECTIVENESS; FACE RECOGNITION; HEURISTIC ALGORITHMS; OPTIMIZATION; STATISTICAL METHODS;

ASYMMETRIC BOOSTING; DECISION-THEORETIC PRINCIPLES; GRADIENT DESCENT; MARGIN DETECTORS;

DECISION THEORY;

EID: 34547963287     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273573     Document Type: Conference Paper

References (18)

1. Chawla, N. V., Lazarevie, A., Hall, L. O., & Bowyer, K. (2003). Smoteboost: Improving prediction of the minority class in boosting. In Proceedings of Principles of Knowledge Discovery in Databases.
2. Domingos, P. (1999). Metacost: a general method for making classifiers cost-sensitive. Proceedings of the fifth ACM SIGKDD. ACM Press.
3. Duda, R., Hart, P. E., & Stork, D. (2001). Pattern classification. New York: Wiley and Sons.
4. Fan, W., Stolfo, S., Zhang, J., & Chan, P. (1999). Adacost: Misclassification cost-sensitive boosting. ICML.
5. Freund, Y., & Schapire, R. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
6. Freund, Y., & Schapire, R. (2004). A discussion of "Process consistency for AdaBoost" by Wenxin Jiang, "On the Bayes-risk consistency of regularized boosting methods" by Gabor Lugosi and Nicolas Vayatis, "Statistical behavior and consistency of classification methods based on convex risk minimization" by Tong Zhang. Annals of Statistics.
7. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Journal of Annals of Statistics.
8. Guo, H., & Viktor, H. L. (2004). Learning from unbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explor. Newsl.
9. Hastie, Tibshirani, & Friedman (2001). The elements of statistical learning. New York: Springer-Verlag Inc.
10. Mason, L., Baxter, J., Bartlett, P., & Frean, M. (2000). Boosting Algorithms as Gradient Descent. NIPS.
11. Park, S.-B., Hwang, S., & Zhang, B.-T. (2003). Mining the risk types of human papillomavirus (hpv) by adacost. International Conference on Database and expert Systems Applications.
12. Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics.
13. Sun, Y., Wong, A. K. C., & Wang, Y. (2005). Parameter inference of cost-sensitive boosting algorithms. Machine Learning and Data Mining in Pattern Recognition,4th International Conference.
14. Ting, K. M. (2000). A comparative study of cost-sensitive boosting algorithms. ICML.
15. Viaene, S., Derrig, R. A., & Dedene, G. (2004). Cost-sensitive learning and decision making for massachusetts pip claim fraud data. International Journal of Intelligent Systems.
16. Viola, P., & Jones, M. (2001). Robust real-time object detection. Proc. 2nd Intl Workshop on Statistical and Computational Theories of Vision Modeling, Learning, Computing and Sampling. Vancouver, Canada.
17. Viola, P., & Jones, M. (2002). Fast and robust classification using asymmetric adaboost and a detector cascade. NIPS.
18. Zadrozny, B., Langford, J., & Abe, N. (2003). A simple method for cost-sensitive learning. Technical Report RC22666, IBM.