Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications

IEEE Transactions on Cybernetics

Volumn 44, Issue 3, 2014, Pages 445-455

  Yang, Pengyi ^a,b   Yoo, Paul D ^c   Fernando, Juanita ^d   Zhou, Bing B ^a   Zhang, Zili ^e,f   Zomaya, Albert Y ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

^b NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

^c KHALIFA UNIVERSITY   (United Arab Emirates)

^d MONASH UNIVERSITY   (Australia)

^e SOUTHWEST UNIVERSITY   (China)

^f DEAKIN UNIVERSITY   (Australia)

Author keywords

Bioinformatics applications; ensemble learning; imbalanced learning; sample subset optimization (SSO); under sampling

Indexed keywords

BIOINFORMATICS APPLICATIONS; ENSEMBLE CLASSIFIERS; ENSEMBLE LEARNING; ENSEMBLE TECHNIQUES; IMBALANCED LEARNING; MACHINE LEARNING PROBLEM; OPTIMIZATION TECHNIQUES; UNDER-SAMPLING;

OPTIMIZATION; SET THEORY;

BIOINFORMATICS;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGY; COMPUTER SIMULATION; MACHINE LEARNING; PROCEDURES; SAMPLE SIZE; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; MACHINE LEARNING; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; SAMPLE SIZE;

EID: 84896880054     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2013.2257480     Document Type: Article

References (40)

1. H. He and E. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263-1284, Sep. 2009.
2. T. Dietterich, "Ensemble methods in machine learning," in Proc. 1st Int. Workshop Multiple Classifier Systems, 2000, pp. 1-15.
3. P. Yang, Z. Zhang, B. Zhou, and A. Zomaya, "Sample subset optimization for classifying imbalanced biological data," in Proc. 15th PAKDD, 2011, pp. 333-344.
4. I. Meyer, "A practical guide to the art of RNA gene prediction," Briefings Bioinformatics, vol. 8, no. 6, pp. 396-414, 2007. (Pubitemid 350186920)
5. J. Zeng, S. Zhu, and H. Yan, "Toward accurate human promoter recognition: A review of currently used sequence features and classification methods," Briefings Bioinformatics, vol. 10, no. 5, pp. 498-508, 2009.
6. S. Sonnenburg, G. Schweikert, P. Philips, J. Behr, and G. Rätsch, "Accurate splice site prediction using support vector machines," BMC Bioinformatics, vol. 8, no. 10, p. S7, 2007.
7. R. Blagus and L. Lusa, "Class prediction for high-dimensional classimbalanced data," BMC Bioinformatics, vol. 11, no. 1, p. 523, 2010.
8. R. Akbani, S. Kwek, and N. Japkowicz, "Applying support vector machines to imbalanced datasets," in Proc. 15th ECML, 2004, pp. 39-50. (Pubitemid 41050079)
9. W. Liu and S. Chawla, "Class confidence weighted knn algorithms for imbalanced data sets," in Proc. 15th PAKDD, 2011, pp. 345-356.
10. N. Chawla, N. Japkowicz, and A. Kotcz, "Editorial: Special issue on learning from imbalanced data sets," ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 1-6, 2004.
11. X. Liu, J. Wu, and Z. Zhou, "Exploratory undersampling for classimbalance learning," IEEE Trans. Syst., Man, Cybern., B Cybern., vol. 39, no. 2, pp. 539-550, Apr. 2009.
12. N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell. Res., vol. 16, pp. 321-357, Jun. 2002. (Pubitemid 43057176)
13. S. García and F. Herrera, "Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy," Evol. Comput., vol. 17, no. 3, pp. 275-306, 2009.
14. K. Kim and S. Cho, "An evolutionary algorithm approach to optimal ensemble classifiers for dna microarray data analysis," IEEE Trans. Evol. Comput., vol. 12, no. 3, pp. 377-388, Jun. 2008.
15. D. Che, Q. Liu, K. Rasheed, and X. Tao, "Decision tree and ensemble learning algorithms with their applications in bioinformatics," Software Tools Algorithms Biol. Syst., vol. 696, pp. 191-199, 2011.
16. U. Braga-Neto and E. Dougherty, "Is cross-validation valid for smallsample microarray classification?" Bioinformatics, vol. 20, no. 3, pp. 374-380, 2004. (Pubitemid 38262769)
17. L. Breiman, "Bagging predictors," Mach. Learning, vol. 24, no. 2, pp. 123-140, 1996. (Pubitemid 126724382)
18. R. Schapire, Y. Freund, P. Bartlett, and W. Lee, "Boosting the margin: A new explanation for the effectiveness of voting methods," Ann. Statist., vol. 26, no. 5, pp. 1651-1686, 1998. (Pubitemid 128376902)
19. M. Islam, X. Yao, S. Shahriar Nirjon, M. Islam, and K. Murase, "Bagging and boosting negatively correlated neural networks," IEEE Trans. Syst., Man, Cybern., B Cybern., vol. 38, no. 3, pp. 771-784, Jun. 2008. (Pubitemid 351796157)
20. D. Nam, S. Yoon, and J. Kim, "Ensemble learning of genetic networks from time-series expression data," Bioinformatics, vol. 23, no. 23, pp. 3225-3231, 2007. (Pubitemid 350187526)
21. Y. Peng, "A novel ensemble machine learning for robust microarray data classification," Comput. Biol. Med., vol. 36, no. 6, pp. 553-573, 2006.
22. L. Kuncheva and C. Whitaker, "Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy," Mach. Learning, vol. 51, no. 2, pp. 181-207, 2003.
23. Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J. Hernandez, and R. Harley, "Particle swarm optimization: Basic concepts, variants and applications in power systems," IEEE Trans. Evol. Comput., vol. 12, no. 2, pp. 171-195, Apr. 2008. (Pubitemid 351546693)
24. R. Batuwita and V. Palade, "Micropred: Effective classification of premiRNAs for human miRNA gene prediction," Bioinformatics, vol. 25, no. 8, pp. 989-995, 2009.
25. M. Remmert, D. Linke, A. Lupas, and J. Söding, "Hhomp- prediction and classification of outer membrane proteins," Nucleic Acids Res., vol. 37, no. 2, pp. W446-W451, 2009.
26. P. Horton and K. Nakai, "A probabilistic classification system for predicting the cellular localization sites of proteins," in Proc. 4th ISMB, 1996, pp. 109-115.
27. T. Rani, S. Bhavani, and R. Bapi, "Analysis of e.coli promoter recognition problem in dinucleotide feature space," Bioinformatics, vol. 23, no. 5, pp. 582-588, 2007. (Pubitemid 46522591)
28. C. Pan, J. Olsen, H. Daub, and M. Mann, "Global effects of kinase inhibitors on signaling networks revealed by quantitative phosphoproteomics, " Molecular Cellular Proteomics, vol. 8, no. 12, pp. 2796-2808, 2009.
29. P. Hornbeck, I. Chabra, J. Kornhauser, E. Skrzypek, and B. Zhang, "Phosphosite: A bioinformatics resource dedicated to physiological protein phosphorylation," Proteomics, vol. 4, no. 6, pp. 1551-1561, 2004. (Pubitemid 38738314)
30. J. Smith, J. Everhart, W. Dickson, W. Knowler, and R. Johannes, "Using the ADAP learning algorithm to forecast the onset of diabetes mellitus," in Proc. Annu. Symp. Comput. Appl. Med. Care, 1988, pp. 261-265.
31. R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J.-J. Schmid, S. Sandhu, K. H. Guppy, S. Lee, and V. Froelicher, "International application of a new probability algorithm for the diagnosis of coronary artery disease," Amer. J. Cardiol., vol. 64, no. 5, pp. 304-310, 1989. (Pubitemid 19187662)
32. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, and M. A. Caligiuri, "Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
33. X. Chen, S. T. Cheung, S. So, S. T. Fan, C. Barry, J. Higgins, K.-M. Lai, J. Ji, S. Dudoit, and I. O. L. Ng, "Gene expression patterns in human liver cancers," Molecular Biol. Cell, vol. 13, no. 6, pp. 1929-1939, 2002. (Pubitemid 34651483)
34. S. Dudoit, J. Fridlyand, and T. Speed, "Comparison of discrimination methods for the classification of tumors using gene expression data," J. Amer. Statist. Assoc., vol. 97, no. 457, pp. 77-87, 2002.
35. U. Ohler, G. C. Liao, H. Niemann, and G. M. Rubin, "Computational analysis of core promoters in the drosophila genome," Genome Biol., vol. 3, no. 12, pp. 81-87, 2002.
36. A. Frank and A. Asuncion. (2010). UCI Machine Learning Repository [Online]. Available:
37. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
38. J. Demšar, "Statistical comparisons of classifiers over multiple data sets," J. Mach. Learning Res., vol. 7, pp. 1-30, Jan. 2006. (Pubitemid 43022939)
39. S. Wang and X. Yao, "Multiclass imbalance problems: Analysis and potential solutions," IEEE Trans. Syst., Man, Cybern., B Cybern., vol. 42, no. 4, pp. 1119-1130, Aug. 2012.
40. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, "A review on ensembles for the class imbalance problem: Baggingboosting-, and hybrid-based approaches," IEEE Trans. Syst., Man, Cybern., C Appl. Rev., vol. 42, no. 4, pp. 463-484, Jul. 2012.