- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Abstract and Applied Analysis
Volume 2013 (2013), Article ID 196256, 6 pages
A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
School of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi, Dalian,
Liaoning 116081, China
Received 28 December 2012; Accepted 25 March 2013
Academic Editor: Jianhong (Cecilia) Xia
Copyright © 2013 Yong Zhang and Dapeng Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002.
- M. Gao, X. Hong, S. Chen, and C. J. Harris, “A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems,” Neurocomputing, vol. 74, pp. 3456–3466, 2011.
- G. Weiss, “Mining with rarity: a unifying framework,” SIGKDD Explorations, vol. 6, no. 1, pp. 7–19, 2004.
- C. Seiffert, T. M. Khoshgoftaar, J. van Hulse, and A. Napolitano, “RUSBoost: a hybrid approach to alleviating class imbalance,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 40, no. 1, pp. 185–197, 2010.
- M. S. Kim, “An effective under-sampling method for class imbalance data problem,” in Proceedings of the 8th Symposium on Advanced Intelligent Systems, pp. 825–829, 2007.
- S. J. Yen and Y. S. Lee, “Cluster-based under-sampling approaches for imbalanced data distributions,” Expert Systems with Applications, vol. 36, no. 3, pp. 5718–5727, 2009.
- X. Y. Liu, J. X. Wu, and Z. H. Zhou, “Exploratory undersampling for class-imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 2, pp. 539–550, 2009.
- C. Drummond and R. C. Holte, “C4.5 decision tree, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,” in Proceedings of the Workshop on Learning from Imbalanced Data Sets II, International Conference on Machine Learning, 2003.
- N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, “SMOTEBoost: improving prediction of the minority class in boosting,” in Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD '03), pp. 107–119, September 2003.
- S. Wang, Z. Li, W. Chao, and Q. Cao, “Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning,” in The International Joint Conference on Neural Networks (IJCNN '12), 2012.
- M. Gao, X. Hong, S. Chen, and C. J. Harris, “Probability density function estimation based over-sampling for imbalanced two-class problems,” in The International Joint Conference on Neural Networks (IJCNN '12), 2012.
- C. Elkan, “The foundations of cost-sensitive learning,” in Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 973–978, 2001.
- B. X. Wang and N. Japkowicz, “Boosting support vector machines for imbalanced data sets,” Knowledge and Information Systems, vol. 25, no. 1, pp. 1–20, 2010.
- Y. Sun, M. S. Kamel, A. K. C. Wong, and Y. Wang, “Cost-sensitive boosting for classification of imbalanced data,” Pattern Recognition, vol. 40, no. 12, pp. 3358–3378, 2007.
- H. Guo and H. L. Viktor, “Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach,” SIGKDD Explorations, vol. 6, no. 1, pp. 30–39, 2004.
- R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to imbalanced datasets,” in Proceedings of the 15th European Conference on Machine Learning (ECML '04), pp. 39–50, Pisa, Italy, September 2004.
- Y. Tang, Y. Q. Zhang, and N. V. Chawla, “SVMs modeling for highly imbalanced classification,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 1, pp. 281–288, 2009.
- J. Wang, J. You, Q. Li, and Y. Xu, “Extract minimum positive and maximum negative features for imbalanced binary classification,” Pattern Recognition, vol. 45, pp. 1136–1145, 2012.
- N. García-Pedrajas, J. Pérez-Rodríguez, and A. de Haro-García, “OligoIS: scalable instance selection for class-imbalanced data sets,” IEEE Transactions on Systems, Man, and Cybernetics B, 2012.
- K. Veropoulos, C. Campbell, and N. Cristianini, “Controlling the sensitivity of support vector machines,” in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 55–60, 1999.
- H. S. Seung, M. Opper, and H. Sompolinsky, “Query by committee,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 287–294, July 1992.
- Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, “Selective sampling using the query by committee algorithm,” Machine Learning, vol. 28, no. 2-3, pp. 133–168, 1997.
- Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” in Proceedings of the 2nd European Conference on Computational Learning Theory, pp. 23–37, 1995.
- M. V. Joshi, V. Kumar, and R. C. Agarwal, “Evaluating boosting algorithms to classify rare classes: comparison and improvements,” in Proceedings of the 1st IEEE International Conference on Data Mining (ICDM '01), pp. 257–264, December 2001.
- T. Fawcett, “ROC graphs: notes and practical considerations for researchers,” Tech. Rep. HPL-2003-4, HP Labs, Palo Alto, Calif, USA, 2003.
- D. Lewis and W. Gale, “Training text classifiers by uncertainty sampling,” in Proceedings of the 7th Annual International ACM SIGIR Conference on Research and Development in Information, pp. 73–79, New York, NY, USA, 1998.
- A. Frank and A. Asuncion, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2010, http://archive.ics.uci.edu/ml/.