Using SVM with adaptively asymmetric MisClassification costs for mine-like objects detection

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 2, Issue , 2012, Pages 78-82

  Wang, Xiaoguang ^a   Shao, Hang ^a   Japkowicz, Nathalie ^b   Matwin, Stan ^a   Liu, Xuan ^a   Bourque, Alex ^c   Nguyen, Bao ^c  

^a UNIVERSITY OF OTTAWA   (Canada)

^b NORTHERN ILLINOIS UNIVERSITY   (United States)

^c DEFENCE RESEARCH AND DEVELOPMENT CANADA   (Canada)

Author keywords

Adaptive Asymmetric Misclassification cost; G mean; Imbalanced data sets; Support vector machines

Indexed keywords

CLUTTER OBJECTS; G-MEAN; IMBALANCED DATA SETS; MINE COUNTER MEASURES; MINE-LIKE OBJECTS; MISCLASSIFICATION COSTS; NATURALLY OCCURRING; PREDICTION PERFORMANCE; REAL WORLD DATA; SONAR IMAGE;

COSTS;

SUPPORT VECTOR MACHINES;

EID: 84873604738     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.227     Document Type: Conference Paper

References (13)

1. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16: 321-357, 2002. (Pubitemid 43057176)
2. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced training sets: One-sided selection. Proceddings of the Fourteenth International Conference on Machine Learning, 179-186.
3. D. Williams, V. Myers, and M. Silvious, "Mine Classification with Imbalanced Data," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 3, pp. 528532, July 2009.
4. Chen C., Liaw, A., and Breiman, L. (2004). Using random forest to learn unbalanced data. Technical Report 666, Statistics Department, University of California at Berkeley.
5. H. Guo and HL Viktor (2004). Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach, ACM SIGKDD Explorations, 6(1), 30-39.
6. Akbani, R., Kwek, S., and Japkowicz, N. (2004), Applying Support Vector Machines to Imbalanced Datasets,in the Proceedings of the 2004 European Conference on Machine Learning (ECML'2004-;).
7. Morik, K., Brockhausen, P., Joachims, T. (1999). Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. ICML: 268277
8. Shawe-Taylor, J. and Cristianini, N. (1999) Further results on the margin distribution. In Proceedings of the 12th Conference on Computational Learning Theory, 278-285. (Pubitemid 32213966)
9. Veropoulos, K., Campbell, C., & Cristianini, N. (1999). Controlling the sensitivity of support vector machines. Proceedings of the International Joint Conference on Artificial Intelligence, 55-60.
10. Wang, B. X. and Japkowicz, N., Boosting Support Vector Machines for Imbalanced Data Sets, in the Proceedings of the 17th International Symposium on Methodologies for Intelligent Systems (ISMIS'2008) .
11. Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:127:27, 2011.
12. Chin-Wei Hsu, Chih-Chung Chang and Chih-Jen Lin (2010). A practical guide to support vector classification. Technical Report, National Taiwan University.
13. Bergstra, James; Bengio, Yoshua (2012). "Random Search for Hyper- Parameter Optimization". J. Machine Learning Research 13: 281-305.