Combination approach of SMOTE and biased-SVM for imbalanced datasets

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 228-231

  Wang, He Yong ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; LEARNING SYSTEMS; NEURAL NETWORKS; SAMPLING; SUPPORT VECTOR MACHINES;

CLASSIFIER PERFORMANCES; DATA SETS; IMBALANCED DATA SETS; K-NEAREST NEIGHBORS; NEW APPROACHES; SAMPLING ALGORITHMS; SAMPLING TECHNIQUES; SUPPORT VECTORS;

VECTORS;

EID: 56349163288     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4633794     Document Type: Conference Paper

References (13)

1. N. Chawla, K. Bowyer, L. Hall, P. Kegelmeyer. "SMOTE: Synthetic Minority Over-Sampling Technique", Journal of Artificial Intelligence Research, 2002, 16:321-357.
2. Kubat, M., Matwin, S., "Addressing the Course of Imbalanced Training Sets: One-sided Selection", ICML, 1997:179-186.
3. Joshi, M., Kumar, V., Agarwal, R., "Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements", First IEEE International Conference on Data Mining, 2001:257-264.
4. Wu, G., Chang, E., "Class-Boundary Alignment for Imbalanced Dataset Learning", The Twentieth International Conference on Machine Learning (ICML) Workshop on Learning from. Imbalanced Datasets, Washington DC, 2003, 8:49-56.
5. Huang, K., Yang, H., King, I., Lyu., M. R., "Learning Classifiers from Imbalanced Data Based on Biased Mini-max Probability Machine", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004: 558-563.
6. Peng, X., Irwin K., "Efficient Training on Biased Minimax Probability Machine for Imbalanced Text Classification Efficient Training on Biased Minimax Probability Machine for Imbalanced Text Classification", In Proceedings of WWW 2007, ACM, 2007, 1153-1154.
7. Seyda, E., Jian, H., Léon B., and Lee, C. G., "Learning on the Border: Active Learning in Imbalanced Data Classification", Proceedings of the 16th Conference on Information and Knowledge Management, CKM2007, ACM Press, 2007.
8. Wu, G., Chang, E.Y., "Aligning Boundary in Kernel Space for Learning Imbalanced Dataset", ICDM, (2004) 265-272.
9. Veropoulos, K., Campbell, C., Cristianini, N., "Controlling the Sensitivity of Support Vector Machines", Proceedings of the International Joint Conference on AI, 1999: 55-60.
10. Blake, C., Merz, C., "UCI Repository of Machine Learning Databases", Department of Information, and Computer Sciences, University of California, Irvine, 1998, available from http://www.ics.uci.edu/~mlearn/ ~MLRepository.html.
11. Webb, A. R., Statistical Pattern Recognition (Second Edition), Beijing: Publishing House of Electronics Industry, 2004, 10:33-37.
12. Vapnik, V., The Nature of Statistical Learning Theory, Springer-Verlag, NY, 1995.
13. Akbani, R., Kwek, S., Japkowicz, N., "Applying Support Vector Machines to Imbalanced Datasets", European Conference on Machine Learning, 2004, 39-50.