Optimization of SVM parameters for recognition of regulatory DNA sequences

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Volumn 18, Issue 2, 2010, Pages 339-353

  Damaševičius, Robertas ^a  

^a KAUNAS UNIVERSITY OF TECHNOLOGY   (Lithuania)

Author keywords

Bioinformatics; Machine learning; Promoter recognition; Support Vector Machine; SVM parameter optimization

Indexed keywords

EID: 78650310572     PISSN: 11345764     EISSN: 18638279     Source Type: Journal    
DOI: 10.1007/s11750-010-0152-x     Document Type: Article

References (40)

1. Ali S, Smith KA (2003) Automatic parameter selection for polynomial kernel. In: Proc of the IEEE int conf on information reuse and integration (IRI 2003), October 27-29, 2003, Las Vegas, NV, USA, pp 243-249.
2. Ancona N, Cicirelli G, Stella E, Distante A (2002) Object detection in images: Run-time complexity and parameter selection of Support Vector Machines. In: Proc of the 16th int conf on pattern recognition (ICPR'02), 11-15 August 2002, Quebec, Canada, vol 2, pp 426-429.
3. Ayat NE, Cheriet M, Suen CY (2002) Empirical error based optimization of SVM kernels: Application to digit image recognition. In: Proc of the 8th int workshop on frontiers in handwriting recognition (IWFHR'02), August 6-8, 2002, p 292.
4. Boardman M, Trappenberg T (2006) A heuristic for free parameter optimization with Support Vector Machines. In: Proc of IEEE int joint conf on neural networks (IJCNN 2006), July 16-21, 2006, Vancouver, Canada, pp 1337-1344.
5. Brunak S, Engelbrecht J, Knudsen S (1991) Prediction of human mRNA donor and acceptor sites from the DNA sequence. J Mol Biol 220: 49-65.
6. Cassabaum ML, Waagen DE, Rodriguez JJ, Schmitt HA (2004) Unsupervised optimization of Support Vector Machine parameters. In: Kadar I (ed) Automatic target recognition XIV. Proc of SPIE, vol 5426(1), SPIE Defense & Security Symposium, Orlando, FL, April 13-15, 2004, pp 316-325.
7. Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1-3): 131-159.
8. Cherkassky V, Mulier F (1998) Learning from data: concepts, theory, and methods. Wiley, New York.
9. Christmann A, Luebke K, Rüping S, Marin-Galianos M (2005) Determination of hyperparameters for kernel-based classification and regression. Technical report 38/05, SFB475, University of Dortmund, Germany.
10. Damaševičius R (2008a) Splice site recognition in DNA sequences using k-mer frequency based mapping for Support Vector Machine with power series kernel. In: Proc of int conf on complex software intensive systems (CISIS-2008), March 4-7, 2008, Barcelona, Spain, pp 687-692.
11. Damaševičius R (2008b) Feature representation of DNA sequences for machine learning tasks. In: Proc of fifth int workshop on computational systems biology (WCSB 2008), June 11-13, 2008, Leipzig, Germany, pp 29-32.
12. Damaševičius R (2008c) Analysis of binary feature mapping rules for promoter recognition in imbalanced DNA sequence datasets using Support Vector Machine. In: Proc of IEEE int conf on intelligent systems (IS'08), September 6-8, 2008, Varna, Bulgaria, vol 2, pp 1120-1125.
13. Debnath R, Takahashi H (2004) An efficient method for tuning kernel parameter of the support vector machine. In: Proc of the IEEE int symp on communications and information technology (ISCIT 2004), Sapporo, Japan, October 2004, vol 2, pp 1023-1028.
14. Demeler B, Zhou GW (1991) Neural network optimization for E. coli promoter prediction. Nucleic Acids Res 19: 1593-1599.
15. Duan K, Keerthi SS, Poo AN (2003) Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51: 41-59.
16. Eitrich T, Lang B (2006) Efficient optimization of Support Vector Machine learning parameters for unbalanced data sets. J Comput Appl Math 196(2): 425-436.
17. Friedrichs F, Igel C (2004) Evolutionary tuning of multiple SVM parameters. In: Trends in neurocomputing: 12th European symp on artificial neural networks 2004, vol 64, pp 107-117.
18. Frohlich H, Zell A (2005) Efficient parameter selection for Support Vector Machines in classification and regression via model-based global optimization. In: Proc of IEEE int joint conf on neural networks (IJCNN '05), 31 July-4 Aug 2005, vol 3, pp 1431-1436.
19. Gold C, Sollich P (2005) Fast Bayesian Support Vector Machine parameter tuning with the nystrom method. In: Proc. of the IEEE int joint conf on neural networks (IJCNN '05), July 31-August 4, 2005, Montréal, Québec, Canada, vol 5, pp 2820-2825.
20. Gordon L, Chervonenkis A, Gammerman AJ, Shahmuradov IA, Solovyev VV (2003) Sequence alignment kernel for recognition of promoter regions. Bioinformatics 19: 1964-1971.
21. Imbault F, Lebart K (2004) A stochastic optimization approach for parameter tuning of support vector machines. In: Proc of the 17th int conf on pattern recognition (ICPR 2004), 23-26 August 2004, Cambridge, UK, vol 4, pp 597-600.
22. Kulkarni A, Jayaraman VK, Kulkarni BD (2004) Support vector classification with parameter tuning assisted by agent-based technique. Comput Chem Eng 28(3): 311-318.
23. Kurasova O, Dzemyda G, Vainoras A (2007) Parameter system for human physiological data representation and analysis. In: Proc of 3rd Iberian conf on pattern recognition and image analysis, IbPRIA 2007, Girona, Spain, June 6-8, 2007. LNCS, vol 4477, pp 209-216.
24. Lim H (2004). Support vector parameter selection using experimental design based generating set search (SVEG) with application to predictive software data modeling. PhD thesis, Syracuse University.
25. Lin C-J, Peng C-C, Lee C-Y (2004) Prediction of RNA polymerase binding sites using purine-pyrimidine encoding and hybrid learning methods. Int J Appl Sci Eng 2: 177-188.
26. Liu YA, Stoller SD, Teitelbaum T (1998) Static caching for incremental computation. ACM Trans Program Lang Syst 20(3): 546-585.
27. Mattera D, Haykin S (1999) Support vector machines for dynamic reconstruction of a chaotic system. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods: support vector learning, pp 209-241.
28. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4): 308-313.
29. Platt J (2000) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. MIT Press, Cambridge.
30. Quan Y, Yang J (2003) An improved parameter tuning method for support vector machines. In: Proc of 9th int conf on rough sets, fuzzy sets, data mining, and granular computing (RSFDGrC 2003), Chongqing, China, May 26-29, 2003, pp 607-610.
31. Ranawana R, Palade V (2005) A neural network based multiclassifier system for gene identification in DNA sequences. J Neural Comput Appl 14: 122-131.
32. Raudys S (2005) Texonomy of classifiers based on dissimilarity features. In: Proc of 3rd int conf on advances in pattern recognition, ICAPR 2005, Bath, UK, August 22-25, 2005. LNCS, vol 3686, pp 136-145.
33. Schittkowski K (2005) Optimal parameter selection in Support Vector Machines. J Ind Manag Optim 1(4): 465-476.
34. Smola AJ, Murata N, Schölkopf B, Miller KR (1998) Asymptotically optimal choice of ε-loss for support vector machines. In: Proc of 8th int conference on artificial neural networks, Berlin, Germany, pp 105-110.
35. Sobha Rani T, Durga Bhavani S, Bapi RS (2007) Analysis of E. coli promoter recognition problem in dinucleotide feature space. Bioinformatics 23(5): 582-588.
36. Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New York.
37. van der Walt CM, Barnard E (2006) Data characteristics that determine classifier performance. In: Proc of the 16th annual symp of the pattern recognition association of South Africa, pp 160-165.
38. Werner T (2003) The state of the art of mammalian promoter recognition. Brief Bioinform 4(1): 22-30.
39. Yan B, Domeniconi C (2006) Kernel optimization using pairwise constraints for semi-supervised clustering. Technical report ISE-TR-06-09, Information and Software Engineering Department, George Mason University, Fairfax, Virginia, USA.
40. Zhuang L, Dai H (2006) Parameter optimization of kernel-based one-class classifier on imbalance learning. J Comput 1(7): 32-40.