Boosting support vector machines for imbalanced data sets

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4994 LNAI, Issue , 2008, Pages 38-47

  Wang, Benjamin X ^a   Japkowicz, Nathalie ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; ERROR ANALYSIS; PROBLEM SOLVING;

IMBALANCED DATA SETS; MINORITY CLASS; SKEWED VECTOR;

SUPPORT VECTOR MACHINES;

EID: 44649170363     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-68123-6_4     Document Type: Conference Paper

References (14)

1. Akbani, R., Kwek, S., Japkowicz, N.: Applying Support Vector Machines to Imbalanced Datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, Springer, Heidelberg (2004)
2. Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12, 783-789 (1999)
3. Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.P.: SMOTE: synthetic minority oversampling technique. In: International Conference on Knowledge Based Computer Systems (2000)
4. Chawla, N., Lazarevic, A., Hall, L., Bowyer, K.: SMOTEBoost: Improving Prediction of the Minority Class in Boosting. In: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, pp. 107-119 (2003)
5. Chen, C., Liaw, A., Breiman, L.: Using random forest to learn unbalanced data. Technical Report 666, Statistics Department, University of California at Berkeley (2004)
6. Fan, W., Stolfo, S., Zhang, J., Chan, P.: AdaCost: Misclassification Cost-Sensitive Boosting, In: Proceedings of 16th International Conference on Machine Learning, Slovenia (1999)
7. Guo, H., Viktor, H.L.: Learning from Imbalanced. Data Sets with Boosting and Data Generation: The DataBoost-IM Approach. ACM SIGKDD Explorations 6(1), 30-39 (2004)
8. Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis 6(5), 429-450 (2002)
9. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided, selection. In: Proceddings of the Fourteenth International Conference on Machine Learning, pp. 179-186 (1997)
10. Morik, K., Brockhausen, P., Joachims, T.: Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. In: ICML, pp. 268-277 (1999)
11. Shawe-Taylor, J., Cristianini, N.: Further results on the margin distribution. In: Proceedings of the 12th Conference on Computational Learning Theory (1999)
12. Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)
13. Veropoulos, K., Campbell, C., Cristianini, N.: Controlling the sensitivity of support vector machines. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 55-60 (1999)
14. Wu, G., Chang, E.: Adaptive feature-space conformal transformation for imbalanced data learning. In: Proceedings of the 20th International Conference on Machine Learning (2003)