Granular SVM with repetitive undersampling for highly imbalanced protein homology prediction

2006 IEEE International Conference on Granular Computing

Volumn , Issue , 2006, Pages 457-460

  Tang, Yuchun ^a,b   Zhang, Yan Qing ^a,b  

^a IEEE   (United States)

^b GEORGIA STATE UNIVERSITY   (United States)

Author keywords

Bioinformatics; Data mining; Granular computing; Protein homology prediction; Support vector machines

Indexed keywords

BIOINFORMATICS; GRANULAR COMPUTING; PROTEIN HOMOLOGY PREDICTION; SUPPORT VECTOR MACHINES;

ALGORITHMS; INFORMATION ANALYSIS; LEARNING SYSTEMS; PROBLEM SOLVING;

PROTEINS;

EID: 33751098785     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. N. V. Chawla, N. Japkowicz and A. Kolcz, "Editorial: Special Issue on Learning from Imbalanced Data Sets," SIGKDD Explorations 6(1): 1-6 (2004).
2. G. M. Weiss, "Mining with Rarity: A Unifying Framework," SIGKDD Explorations 6(1): 7-19 (2004).
3. N. Japkowicz and S. Stephen, "The Class Imbalance Problem: A Systematic Study," Intelligent Data Analysis Journal, Volume 6, Number 5, November 2002.
4. R. Akbani, S. Kwek, and N. Japkowicz, "Applying Support Vector Machines to Imbalanced Datasets," Proceedings of the 2004 European Conference on Machine Learning (ECML2004), pp.39-50, 2004.
5. B. Raskutti and A. Kowalczyk, "Extreme re-balancing for SVMs: a case study," SIGKDD Explorations 6(1): 60-69 (2004).
6. G. Wu and E. Y. Chang, "KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution," IEEE Transactions on Knowledge and Data Engineering, pp. 786-795, Vol. 17, No. 6, June 2005.
7. 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.
8. th International Conference on Machine Learning (ICML1997), pp.179-186, 1997.
9. N. V. Chawla, A. Lazarevic, L. O. Hall and K. W. Bowyer, "SMOTEBoost: Improving Prediction of the Minority Class in Boosting," in 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 107-119, Cavtat-Dubrovnik, Croatia, Sep. 22-26, 2003.
10. T. Y. Lin, "Data Mining and Machine Oriented Modeling: A Granular Computing Approach," Journal of Applied Intelligence, Kluwer, Vol 13, No 2, 2000, pp. 113-124.
11. W. Pedrycz, Granular Computing: An Emerging Paradigm, Physica-Verlag, 2001.
12. Y.C. Tang, B. Jin and Y.-Q. Zhang, "Granular Support Vector Machines with Association Rules Mining for Protein Homology Prediction," Special Issue on Computational Intelligence Techniques in Bioinformatics, Artificial Intelligence in Medicine, Vol. 35, No. 1-2, pp. 121-134, 2005.
13. th annual IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2005), pp.138-142, Reno, 2005.
14. Y.C. Tang, Y.-Q. Zhang, Z. Huang, X. Tony Hu and Y. Zhao, "Granular SVM-RFE Feature Selection Algorithm for Reliable Cancer-Related Gene Subsets Extraction on Microarray Gene Expression Data," Special Issue on Bioinformatics, Pattern Recognition, (accepted), 2006.
15. J. Ma, Y. Zhao, and S. Ahalt, OSU SVM Classifier Matlab Toolbox, Available at http://www.ece.osu.edu/~maj/osu_svm/.
16. C. -C. Chang, C. -J. Lin, LIBSVM: a library for support vector machines, 2001. Software Available at http://www.csie.ntu.edu.tw/~cjlin/iibsvm.
17. R. Caruana, The PERF Performance Evaluation Code, http://kodiak.cs. cornell.edu/kddcup/software.html.
18. Y. Fu, R. Sun, Q. Yang, S. He, C. Wang, H. Wang, S. Shan, J. Liu and W. Gao, "A block-based support vector machine approach to the protein homology prediction task in KDD Cup 2004," ACM SIGKDD Explorations Newsletter, vol. 6, no. 2, pp. 120-124, 2004.