Abstract
RNA sequences detection is time-consuming because of its huge data set size. Although SVM has been proved to be useful, normal SVM is not suitable for classification of large data sets because of its high training complexity. A two-stage SVM classification approach is introduced for fast classifying large data sets. Experimental results on several RNA sequences detection demonstrate that the proposed approach is promising for such applications.
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Awad, M.L., Khan, F., Bastani, I., Yen, L.: An Effective support vector machine(SVMs) Performance Using Hierarchical Clustering. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 663–667. IEEE Computer Society Press, Los Alamitos (2004)
Axmann, I.M., Kensche, P., Vogel, J., Kohl, S., Herzel, H., Hess, W.R.: Identification of cyanobacterial non-coding RNAs by comparative genome analysis. Genome Biol. R73 6 (2005)
Babu, G., Murty, M.: A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm. Pattern Recognit. Lett. 14, 763–769 (1993)
Cervantes, J., Li, X., Yu, W.: Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 572–582. Springer, Heidelberg (2006)
Cervantes, J., Li, X., Yu, W., Li, K.: Support vector machine classification for large data sets via minimum enclosing ball clustering. Neurocomputing (accepted for publication)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chen, P.H., Fan, R.E., Lin, C.J.: A Study on SMO-Type Decomposition Methods for Support Vector Machines. IEEE Trans. Neural Networks 17, 893–908 (2006)
Collobert, R., Bengio, S.: SVMTorch: Support vector machines for large regression problems. Journal of Machine Learning Research 1, 143–160 (2001)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
Dong, J.X., Krzyzak, A., Suen, C.Y.: Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 603–618 (2005)
Folino, G., Pizzuti, C., Spezzano, G.: GP Ensembles for Large-Scale Data Classification. IEEE Trans. Evol. Comput. 10, 604–616 (2006)
Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S.R., Bateman, A.: RFAM: annotating non-coding RNAs in complete genomes. Nucleic Acids. Res. 33, 121–124 (2005)
Girolami, M.: Mercer kernel based clustering in feature space. IEEE Trans. Neural Networks 13, 780–784 (2002)
Hansen, J.L., Schmeing, T.M., Moore, P.B., Steitz, T.A.: Structural insights into peptide bond formation. Proc. Natl. Acad. Sci. 99, 11670–11675 (2002)
Huang, G.B., Mao, K.Z., Siew, C.K., Huang, D.S.: Fast Modular Network Implementation for Support Vector Machines. IEEE Trans. on Neural Networks (2006)
Joachims, T.: Making large-scale support vector machine learning practice. Advances in Kernel Methods: Support Vector Machine. MIT Press, Cambridge (1998)
Kim, S.W., Oommen, B.J.: Enhancing Prototype Reduction Schemes with Recursion: A Method Applicable for Large Data Sets. IEEE Trans. Syst. Man, Cybern. B. 34, 1184–1397 (2004)
Li, X., Cervantes, J., Yu, W.: Two Stages SVM Classification for Large Data Sets via Randomly Reducing and Recovering Training Data. In: IEEE International Conference on Systems, Man, and Cybernetics, Montreal Canada (2007)
Lin, C.T., Yeh, L.C.M., S, F., Chung, J.F., Kumar, N.: Support-Vector-Based Fuzzy Neural Network for Pattern Classification. IEEE Trans. Fuzzy Syst. 14, 31–41 (2006)
Mavroforakis, M.E., Theodoridis, S.: A Geometric Approach to Support Vector Machine(SVM) Classification. IEEE Trans. Neural Networks 17, 671–682 (2006)
Noble, W.S., Kuehn, S., Thurman, R., Yu, M., Stamatoyannopoulos, J.: Predicting the in vivo signature of human gene regulatory sequences. Bioinformatics 21, 338–343 (2005)
Pal, N., Bezdek, J.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3, 370–379 (1995)
Platt, J.: Fast Training of support vector machine using sequential minimal optimization. Advances in Kernel Methods: support vector machine. MIT Press, Cambridge, MA (1998)
Prokhorov, D.: IJCNN 2001 neural network competition. Ford Research Laboratory (2001), http://www.geocities.com/ijcnn/nnc_ijcnn01.pdf
Shih, L., Rennie, D.M., Chang, Y., Karger, D.R.: Text Bundling: Statistics-based Data Reduction. In: Proc. of the Twentieth Int. Conf. on Machine Learning, Washington DC (2003)
Tresp, V.: A Bayesian Committee Machine. Neural Computation 12, 2719–2741 (2000)
Uzilov, A.V., Keegan, J.M., Mathews, D.H.: Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change. BMC Bioinformatics 173 (2006)
Washietl, S., Hofacker, I.L., Lukasser, M., Huttenhofer, A., Stadler, P.F.: Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nat. Biotechnol. 23, 1383–1390 (2005)
Weilbacher, T., Suzuki, K., Dubey, A.K., Wang, X., Gudapaty, S., Morozov, I., Baker, C.S., Georgellis, D., Babitzke, P., Romeo, T.: A novel sRNA component of the carbon storage regulatory system of Escherichia coli. Mol. Microbiol. 48, 657–670 (2003)
Xu, R., WunschII, D.: Survey of Clustering Algorithms. IEEE Trans. Neural Networks 16, 645–678 (2005)
Yu, H., Yang, J., Han, J.: Classifying Large Data Sets Using SVMs with Hierarchical Clusters. In: Proc. of the 9th ACM SIGKDD, ACM Press, New York (2003)
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Li, X., Li, K. (2007). Detecting RNA Sequences Using Two-Stage SVM Classifier. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_2
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DOI: https://doi.org/10.1007/978-3-540-74771-0_2
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