Misclassification analysis for the class imbalance problem

2010 World Automation Congress, WAC 2010

Volumn , Issue , 2010, Pages

  Jeatrakul, Piyasak ^a   Wong, Kok Wai ^a   Fung, Chun Che ^a   Takama, Yasufumi ^b  

^a MURDOCH UNIVERSITY   (Australia)

^b TOKYO METROPOLITAN UNIVERSITY   (Japan)

Author keywords

Artificial neural network; Class imbalance problem; Classification; Complementary neural network; Misclassification analysis

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLASS IMBALANCE PROBLEMS; CLASSIFICATION; COMPLEMENTARY NEURAL NETWORK; MISCLASSIFICATION ANALYSIS;

LEARNING ALGORITHMS;

NEURAL NETWORKS;

EID: 78651444178     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. G. E. A. P. A. Batista, R. C. Prati, and M. C. Monard, "A study of the behavior of several methods for balancing machine learning training data," SIGKDD Explorations Newsletter, vol. 6, pp. 20-29, 2004.
2. Q. Gu, Z. Cai, L. Zhu, and B. Huang, "Data mining on imbalanced data sets," in Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on, 2008, pp. 1020-1024.
3. I. Tomek, "Two Modifications of CNN," Systems, Man and Cybernetics, IEEE Transactions on, vol. 6, pp. 769-772, 1976.
4. M. Kubat and S. Matwin, "Addressing the curse of imbalanced training sets: One-sided selection," in the Fourteenth International Conference on Machine Learning, 1997, pp. 179-186.
5. P. Hart, "The condensed nearest neighbor rule " Information Theory, IEEE Transactions on, vol. 14, pp. 515-516, 1968.
6. D. L. Wilson, "Asymptotic properties of nearest neighbor rules using edited Data," Systems, Man and Cybernetics, IEEE Transactions on, vol. 2, pp. 408-421, 1972.
7. J. Laurikkala, "Improving identification of difficult small classes by balancing class distribution," in Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine: Springer-Verlag, 2001.
8. 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.
9. J. Gu, Y. Zhou, and X. Zuo, "Making class bias useful: A strategy of learning from imbalanced data," in Intelligent Data Engineering and Automated Learning - IDEAL 2007, 2007, pp. 287-295.
10. R. Barandela, J. S. Sanchez, V. Garcia, and E. Rangel, "Strategies for learning in class imbalance problems," Pattern Recognition, vol. 36, pp. 849-851, 2003.
11. P. Kraipeerapun, C. C. Fung, and S. Nakkrasae, "Porosity prediction using bagging of complementary neural networks," in Advances in Neural Networks - ISNN 2009, 2009, pp. 175-184.
12. A. Asuncion and D. J. Newman, "UCI Machine Learning Repository," University of California, Irvine, School of Information and Computer Sciences, 2007.
13. P. Kraipeerapun and C. C. Fung, "Binary classification using ensemble neural networks and interval neutrosophic sets," Neurocomput., vol. 72, pp. 2845-2856, 2009.
14. P. Kraipeerapun and C. C. Fung, "Comparing performance of interval neutrosophic sets and neural networks with support vector machines for binary classification problems," in Digital Ecosystems and Technologies, 2008. DEST 2008. 2nd IEEE International Conference on, 2008, pp. 34-37.