Abstract
Protein fold classification is the prediction of protein’s tertiary structure (fold) from amino acid sequence without relying on the sequence similarity. The problem how to predict protein fold from amino acid sequence is regarded as a great challenge in computational biology and bioinformatics. To deal with this problem the support vector machine (SVM) classifier was introduced. However the SVM is a binary classifier, but protein fold recognition is a multi-class problem. So the method of solving this issue was proposed based on error correcting output codes (ECOC). The key problem in this approach is how to construct the optimal ECOC codewords. There are three strategies presented in this paper based on recognition ratios obtained by binary classfiers on the traing data set. The SVM classifier using the ECOC codewords contructed using these strategies was used on a real world data set. The obtained results (57.1% - 62.6%) are better than the best results published in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allwein, E., Schapire, R., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2002)
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C., Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412–424 (2000)
Chan, H.S., Dill, K.: The protein folding problem. Physics Today, 24–32 (February 1993)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chung, I.F., Huang, C.D., Shen, Y.H., Lin, C.T.: Recognition of structure classification of protein folding by NN and SVM hierarchical learning architecture. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1159–1167. Springer, Heidelberg (2003)
Ding, C.H., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001)
Dietterich, T.G., Bakiri, G.: Solving multiclass problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Dubchak, I., Muchnik, I., Holbrook, S.R., Kim, S.H.: Prediction of protein folding class using global description of amino acid sequence. Proc. Natl. Acad. Sci. USA 92, 8700–8704 (1995)
Dubchak, I., Muchnik, I., Kim, S.H.: Protein folding class predictor for SCOP: approach based on global descriptors. In: Proceedings ISMB, vol. 5, pp. 104–107 (1997)
Fei, B., Liu, J.: Binary Tree of SVM: A New Fast Multiclass Training and Classification Algorithm. IEEE Transaction on Neural Networks 17(3), 696–704 (2006)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Annals of Statistics 26(2), 451–471 (1998)
Hobohm, U., Scharf, M., Schneider, R., Sander, C.: Selection of a representative set of structures from the Brookhaven Protein Bank. Protein Sci. 1, 409–417 (1992)
Hobohm, U., Sander, C.: Enlarged representative set of Proteins. Protein Sci. 3, 522–524 (1994)
Lo Conte, L., Ailey, B., Hubbard, T.J.P., Brenner, S.E., Murzin, A.G., Chotchia, C.: SCOP: a structural classification of protein database. Nucleic Acids Res. 28, 257–259 (2000)
Nanni, L.: A novel ensemble of classifiers for protein fold recognition. Neurocomputing 69, 2434–2437 (2006)
Okun, O.: Protein fold recognition with k-local hyperplane distance nearest neighbor algorithm. In: Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics, Pisa, Italy, September 24, pp. 51–57 (2004)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. In: Proceedings of Neural Information Processing Systems, pp. 547–553 (2000)
Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vural, V., Dy, J.G.: A hierarchical method for multi-class support vector machines. In: Proceedings of the Twenty-First ICML, pp. 831–838 (2004)
Windeatt, T., Ghaderi, R.: Coding and decoding for multiclass learning problems. Information Fusion 4(1), 11–21 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chmielnicki, W., Stąpor, K. (2011). A New Approach to Multi-class SVM-Based Classification Using Error Correcting Output Codes. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-20320-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20319-0
Online ISBN: 978-3-642-20320-6
eBook Packages: EngineeringEngineering (R0)