Abstract
Evolutionary and natural computing techniques have been drawn considerable interest for analyzing large datasets with large number of features. Various flavors of Particle Swarm Optimization (PSO) have been applied in the various research applications like Control and Automation, Function Optimization, Dimensionality Reduction, classification. In the present work, we have applied the SVM based classifier along with Novel PSO and Binary PSO on Huesken dataset of siRNA features as well as on nine other benchmark dataset and achieved results are quite satisfactory. The results of our study have been compared with other results available 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
Saetrom, P., Snove, O.: A comparison of siRNA efficacy predictors. Biochem. Biophys. Re. Commun. 321(1), 247–253 (2004)
Huesken, D., Lange, J., Mickanin, C., Weiler, J., Asselbergs, F., Warner, J., Meloon, B., Engel, S., Rosenberg, A., Cohen, D., Labow, M., Reinhardt, M., Natt, F., Hall, J.: Design of a genome-wide siRNA library using an artificial neural network. Nat. Biotechnology 23, 995–1001 (2005)
Vert, J.P., Foveau, N., Lajaunie, C., Vandenbrouck, Y.: An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinformatics 7, 520 (2006)
Matveeva, O., Nechipurenko, Y., Rossi, L., Moore, B., Sætrom, P., Ogurtsov, A.Y., Atkins, J.F., Shabalina, S.A.: Comparison of approaches for rational siRNA design leading to a new efficient and transparent method. Nucleic Acids Res. 35, e63 (2007)
Xiaowei, W., Xiaohui, W., Verma, R.K., Beauchamp, L., Maghdaleno, S., Surendra, T.J.: Selection of Hyperfunctional siRNAs with improved potency and specificity. Nucleic Acids Research 37(22), e152 (2009)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Chih-Chung, C., Chih-Jen, L.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support Vector Regression Machines. Advances in Neural Information Processing Systems, NIPS 9, 155–161 (1997)
Tsang, C.H.: Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection. In: Swarm Intelligence in Data Mining, pp. 101–123. Springer, Heidelberg (2007)
Nemati, S., Basiri, M.E., Ghasem-Aghaee, N., Aghdam, M.H.: A novel ACO–GA hybrid algorithm for feature selection in protein function prediction. Expert Systems with Applications 36, 12086–12094 (2009)
Aghdam, M.H., Ghasem-Aghaee, N., Basiri, M.E.: Text feature selection using ant colony optimization. Expert Systems with Applications 36, 6843–6853 (2009)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intelligent Systems 13(2), 44–49 (1998)
Zhao, X., Huang, D., Cheung, Y., Wang, H., Huang, X.: A Novel Hybrid GA/SVM System for Protein Sequences Classification. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 11–16. Springer, Heidelberg (2004)
Raymer, M., Punch, W., Goodman, E., Kuhn, L., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computing 4, 164–171 (2000)
Chung-Jui, T., Li-Yeh, C., Jun-Yang, C., Cheng-Hong, Y.: Feature Selection using PSO-SVM. IAENG International Journal of Computer Science 33(1), IJCS_33_1_18 (2007)
Liu, Y., Qin, Z., Xu, Z., He, H.: Feature selection with particle swarms. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 425–430. Springer, Heidelberg (2004)
Khanesar, M.A., Teshnehlab, M., Soorehdeli, M.A.: A Novel Binary Particle Swarm Optimization. In: Proc. 15th Mediterranean Conference on Control and Automation (2007)
Correa, S., Freitas, A.A., Johnson, C.G.: Particle Swarm and Bayesian networks applied to attribute selection for protein functional classification. In: Proc. of the GECCO 2007 Workshop on Particle Swarms, The Second Decade, pp. 2651–2658 (2007)
Jain, C.K., Prasad, Y.: Feature selection for siRNA efficacy prediction using natural computation. In: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 1759–1764. IEEE Press, Los Alamitos (2009)
Wen, X., Cong, W.: Feature Selection: A hybrid approach based on self adaptive Ant Colony and Support Vector Machine. In: International Conference on Computer Science and Software Engineering (CSSE 2008), pp. 751–754. IEEE Press, Los Alamitos (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prasad, Y., Biswas, K.K. (2010). PSO - SVM Based Classifiers: A Comparative Approach. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-14834-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14833-0
Online ISBN: 978-3-642-14834-7
eBook Packages: Computer ScienceComputer Science (R0)