LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data

BioData Mining

Volumn 6, Issue 1, 2013, Pages

  Nakamura, Munehiro ^a   Kajiwara, Yusuke ^b   Otsuka, Atsushi ^a   Kimura, Haruhiko ^a  

^a KANAZAWA UNIVERSITY   (Japan)

^b RITSUMEIKAN UNIVERSITY   (Japan)

Author keywords

Biomedical data; Learning Vector Quantization; Over sampling; Synthetic Minority Over sampling Technique

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BETA TURN; BIOINFORMATICS; BIOMEDICINE; CLASSIFICATION ALGORITHM; CLINICAL EFFECTIVENESS; CONTROLLED STUDY; HUMAN; LEARNING VECTOR QUANTIFICATION; MACHINE LEARNING; PERFORMANCE; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SAMPLING; SMOTE ALGORITHM; VISUAL CORTEX;

EID: 84884791050     PISSN: None     EISSN: 17560381     Source Type: Journal    
DOI: 10.1186/1756-0381-6-16     Document Type: Article

References (26)

1. MicroPred: effective classification of pre-miRNAs for human miRNA gene prediction. Batuwita R, palade V, Bioinformatics 2009 25 8 989 995 10.1093/bioinformatics/btp107 19233894
2. Predicting protein-protein interactions in unbalanced data using the primary structure of proteins. Yu C, Chou L, Chang D, BMC Bioinformatics 2010 11 167 1 10 20043860
3. Learning from imbalanced data. Haibo H, IEEE Trans Knowledge Data Eng 2009 21 9 1263 1284
4. Boosting a weak learning algorithm by majority. Freund Y, Inform Comput 1995 121 2 256 285 10.1006/inco.1995.1136
5. Quinlan R, C4.5: Proggrams for Machine Learning San Francisco: Morgan Kaufmann Publishers 1993
6. Random forests. Breiman L, Mach Learn 2001 45 5 32 10.1023/A: 1010933404324 (Pubitemid 32933532)
7. SMOTE: synthetic minority over-sampling technique. Chawla N, Bowyer K, Hall L, Kegelmeyer W, J Art Intell Res 2002 16 321 357 (Pubitemid 43057176)
8. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Han H, Wang WY, Mao BH, Proc of the 2005 International Conference on Advances in Intelligent Computing Hefei: Springer 2005 878 887 (Pubitemid 41491129)
9. RAMOBoost: ranked minority oversampling in boosting. Shen S, He H, Garcia E, IEEE Trans Neural Netw 2010 21 10 1624 1642 20805051
10. MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. Baura S, Islam M, Yao X, Murase K, IEEE Trans Knowledge Data Eng 2012 (PrePrint) doi:10.1109/TKDE.2012.232
11. Learning vector quantization. Kohonen T, The Handbook of Brain Theory and Neural Networks Cambridge: MIT Press 1995 537 540
12. Frank A, Asuncion A, UCI Machine Learning Repository Irvine 2010 http://archive.ics.uci.edu/ml/
13. Kohonen T, LVQ PAK: The Learning Vector Quantization Program Package 1996 http://www.cis.hut.fi/research/lvq-pak/
14. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Alon U, Barkai N, Notterman D, Gish K, Barra S, Mack D, Levine A, Proc Natl Acad Sci USA 1999 96 6745 6750 10.1073/pnas.96.12.6745 10359783 (Pubitemid 29274954)
15. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Golub T, Science 1999 286 5439 531 537 10.1126/science.286.5439.531 10521349
16. High accuracy prediction of beta-turns and their types using propensities and multiple alignments. Fuchs P, Alix A, Proteins 2005 59 4 828 839 10.1002/prot.20461 15822097 (Pubitemid 40695857)
17. A revised set of potentials for beta-turn formation in proteins. Hutchinson E, Thornton J, Protein Sci 1994 3 12 2207 2216 10.1002/pro.5560031206 7756980
18. Predicting β -turns and their types using predicted backbone dihedral angles and secondary structures. Kountouris P, Hirst J, BMC Bioinformatics 2010 11 407 1 11 20043860
19. Support-vector networks. Cortes C, Vapnik V, Mach Learn 1995 20 3 273 297
20. Speeding up logistic model tree induction. Marc S, Eibe F, Mark H, Proc of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases Porto: Springer 2005 675 683
21. Rumelhart D, Hinton G, Williams R, Learning Internal Representations by Error Propagation, Volume 1 Cambridge: MIT Press 1986
22. Etimating continuous distributions in bayesian classifiers. George H, Pat L, Proc of the Eleventh Conference on Uncertainty in Artificial Intelligence San Francisco: Morgan Kaufmann Publishers Inc. 1995 338 345
23. LIBSVM: a library for support vector machines. Chang C, Lin J, ACM Trans Intell Syst Technol 2011 2 27 531 537
24. Weka 3: data mining software in Java. Mark H, Eibe F, Geoffrey H, Bernhard P, Peter R, Ian H, ACM SIGKDD Explorations Newsletter; 2009. Machine Learning Group at the University of waikato. http://www.cs.waikato.ac.nz/ml/ weka/
25. A decision-theoretic generalization of on-line learning and an application to boosting. Yaov F, Robert E, J Comput Syst Sci 1995 55 119 139
26. Prediction of β-turn types in protein by using composite vector. Shi X, Hu X, Li S, Liu X, J Theor Biol 2011 286 1 24 30 21781975