An improved ensemble approach for imbalanced classification problems

SACI 2013 - 8th IEEE International Symposium on Applied Computational Intelligence and Informatics, Proceedings

Volumn , Issue , 2013, Pages 423-426

  Krawczyk, Bartosz ^a   Schaefer, Gerald ^b  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION APPROACH; CLASSIFIER ENSEMBLES; CLASSIFIER SELECTION; ENSEMBLE APPROACHES; ENSEMBLE CONSTRUCTION; IMBALANCED CLASSIFICATION; INDIVIDUAL CLASSIFIERS;

ARTIFICIAL INTELLIGENCE; INFORMATION SCIENCE;

CLASSIFICATION (OF INFORMATION);

EID: 84886457670     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SACI.2013.6609011     Document Type: Conference Paper

References (25)

1. H. He and E. A. Garcia, "Learning from imbalanced data, " IEEE Transactions on Knowledge and Data Engineering, Vol. 21, no. 9, pp. 1263-1284, 2009.
2. W. Khreich, E. Granger, A. Miri, and R. Sabourin, "Iterative boolean combination of classifiers in the roc space: An application to anomaly detection with hmms, " Pattern Recognition, Vol. 43, no. 8, pp. 2732-2752, 2010.
3. Z. Yang, W. H. Tang, A. Shintemirov, and Q. H. Wu, "Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers, " IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 39, no. 6, pp. 597-610, 2009.
4. B. Krawczyk, L. Jelen, A. Krzyzak, and T. Fevens, "Oversampling methods for classification of imbalanced breast cancer malignancy data, " in Computer Vision and Graphics, ser. Lecture Notes in Computer Science, L. Bolc, R. Tadeusiewicz, L. Chmielewski, and K. Wojciechowski, Eds. Springer Berlin / Heidelberg, 2012, Vol. 7594, pp. 483-490.
5. Y. Liu and Y. Chen, "Face recognition using total margin-based adaptive fuzzy support vector machines, " IEEE Transactions on Neural Networks, Vol. 18, no. 1, pp. 178-192, 2007.
6. L. Kuncheva, Combining pattern classifiers: Methods and algorithms. Wiley-Interscience, New Jersey, 2004.
7. B. Krawczyk and G. Schaefer, "Effective multiple classier systems for breast thermogram analysis, " in 21st Int. Conference on Pattern Recognition, 2012.
8. M. Wózniak and M. Zmyslony, "Designing combining classifier with trained fuser-analytical and experimental evaluation, " Neural Network World, Vol. 20, no. 7, pp. 925-934, 2010.
9. V. Lopez, A. Fernandez, J. G. Moreno-Torres, and F. Herrera, "Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics, " Expert Systems with Applications, Vol. 39, no. 7, pp. 6585-6608, 2012.
10. 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.
11. B. Krawczyk, G. Schaefer, and M. Wozniak, "Combining one-class classifiers for imbalanced classification of breast thermogram features, " in 4th Int. Workshop on Computational Intelligence in Medical Imaging, 2013, held as part of IEEE Symposium Series on Computational Intelligence.
12. T. Nakashima, Y. Yokota, H. Ishibuchi, and G. Schaefer, "A cost-based fuzzy system for pattern classification with class importance, " Artificial Life and Robotics, Vol. 12, no. 12, pp. 43-46, 2008.
13. S. Wang and X. Yao, "Diversity analysis on imbalanced data sets by using ensemble models."
14. N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, "Smoteboost: Improving prediction of the minority class in boosting, " in Lecture Notes in Artificial Intelligence, Vol. 2838, 2003, pp. 107-119.
15. J. Blaszczynski, M. Deckert, J. Stefanowski, and S. Wilk, "Integrating selective pre-processing of imbalanced data with ivotes ensemble, " ser. Lecture Notes in Computer Science, 2010, Vol. 6086 LNAI, pp. 148-157.
16. 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.
17. B. Krawczyk, M. Wozniak, and G. Schaefer, "Improving minority class prediction using cost-sensitive ensembles, " in 16th Online World Conference on Soft Computing in Industrial Applications, 2011.
18. X. Liu, J. Wu, and Z. Zhou, "Exploratory undersampling for classimbalance learning, " IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 39, no. 2, pp. 539-550, 2009.
19. B. Krawczyk and G. Schaefer, "Evolutionary multiple classifier system based on space partitioning for breast thermogram analysis, " in 16th Online World Conference on Soft Computing in Industrial Applications, 2011.
20. L. Yu and H. Liu, "Efficient feature selection via analysis of relevance and redundancy, " Journal of Machine Learning Research, pp. 1205-1224, 2004.
21. G. Giancinto and F. Roli, "Design of effective neural network ensembles for image classification purposes, " Image Vision and Computing Journal, Vol. 19, pp. 699-707, 2001.
22. V. Vapnik, Statistical Learning Theory. Willey, 1998.
23. A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis, "Kernlab, an S4 package for kernel methods in R, " Journal of Statistical Software, Vol. 11, no. 9, 2004.
24. N. Chawla, A. Lazarevic, L. Hall, and K. Bowyer, "Smoteboost: Improving prediction of the minority class in boosting, " in Lecture Notes in Artificial Intelligence, Vol. 2838, 2003, pp. 107-119.
25. E. Alpaydin, "Combined 5 x 2 cv f test for comparing supervised classification learning algorithms, " Neural Computation, Vol. 11, no. 8, pp. 1885-1892, 1999.