Abstract
This paper presents a novel approach called Resilient Voting Scheme (RVS), which combines different predictors (experts) with the goal of improving the overall accuracy. As combining multiple experts involves uncertainty and imprecise information, the proposed approach cancels out the impact of bad performers while computing a single collective prediction. RVS uses a genetic algorithm to assign a reliability to each expert, by using the Q3 measure as fitness function. A resilient voting is then used to improve the accuracy of the final prediction. RVS has been tested with well known datasets and has been compared with other state-of-the-art combination techniques (i.e., averaging and stacking). Experimental results demonstrate the validity of the approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Al-Ani, A., Duriche, M.: A new technique for combining multiple classifiers using dempster-shafer theory of evidence. Artificial Intelligence Research 17, 333–361 (2002)
Armano, G., Mancosu, G., Milanesi, L., Orro, A., Saba, M., Vargiu, E.: A hybrid genetic-neural system for predicting protein secondary structure. BMC Bioinformatics 6, S3 (2005)
Bystroff, C., Thousson, V., Baker, D.: Hmm-str: A hidden markov model for local sequence structure correlations in proteins. J. Mol. Biol. 301, 173–190 (2000)
Chou, P.Y., Fasman, G.D.: Conformational parameters for amino acids in helical, sheet, and random coil regions calculated from proteins. Biochemistry 13, 211–222 (1974)
Cuff, J.A., Barton, G.J.: Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins: Structure, Function, and Genetics 34, 508–519 (1999)
Eyrich, A.V., Marti-Renom, M.A., Przybylski, D., Madhusudhan, M.S., Fiser, A., Pazos, F., Valencia, A., Sali, A., Rost, B.: Eva: continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17(12), 1242–1243 (2001)
Johansson, R., Bostrom, H., Karlsson, A.: A study on class-specifically discounted belief for ensemble classifiers. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 614–619 (2008)
Jones, D.T.: Protein secondary structure prediction based on position specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)
Ledda, F.: Protein secondary structure prediction: Novel methods and software architectures. In: PhD Thesis, DIEE, University of Cagliari, Italy (2011)
Ledda, F., Milanesi, L., Vargiu, E.: Game: A generic architecture based on multiple experts for predicting protein structures. Communications of SIWN 3, 107–112 (2008)
Lei, C., Karel, M.S.: A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems. Pattern Recognition 42, 629–644 (2009)
Lin, H.-N., Chang, J.-M., Wu, K.-P., Sung, T.-Y., Hsu, W.-L.: A knowledge based hybrid method for protein secondary structure prediction based on local prediction confidence. Bioinformatics 21, 3227–3233 (2005)
Lin, K.-L., Lin, C.Y., Huang, C.D., Chang, H.M., Yang, C.Y., Lin, C.T., Tang, C.Y., Hsu, D.F.: Feature seclection and combination criteria for improving accuracy in protein structure prediction. IEEE Transactions on Nanobioscience 6, 186–196 (2007)
Liu, Y.C., Lin, C.-Y., Yu, K.-M., Tang, C.-Y.: Rap: Refine a prediction of protein secondary structure. In: Proceedings of 23rd Workshop on Combinatorial Mathematics and Computation Theory, pp. 34–38 (2006)
Maclin, R., Shavlik, J.W.: Combining the predictions of multiple classifiers: Using competitive learning to initialize neural networks. In: Proceedings of 14th International Joint Conference on Artificial intelligence (IJCAI 1995), pp. 524–531 (1995)
Matthews, B.W.: Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975)
Mount, D.: Bioinformatics: Sequence and Genome Analysis, 2nd edn. CSHL Press, NY (2004)
Qian, N., Sejnowski, T.J.: Predicting the secondary structure of globular proteins using neural network models. Journal of Mol. Biology 202, 865–884 (1988)
Quali, M., King, R.D.: Cascaded multiple classifiers for secondary structure prediction. Protein Science 9, 1162–1176 (2000)
Robert, B., G-Osuna, R., Quek, F.: Attribute bagging: Improving accuracy of classifier ensembles by using random feature subset. Pattern Recognition 36, 1291–1302 (2003)
Rost, B.: Review: Protein secondary structure prediction continues to rise. J. Struct. Biol. 134, 204–218 (2001)
Rost, B., Sander, C.: Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599 (1993)
Rus, S.K., Krogh, A.: Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J. Comp. Biol. 3, 163–183 (1996)
Taheri, J., Zomaya, A.Y.: A voting scheme to improve the secondary structure prediction. In: Proceedings of ACS/IEEE International Conference on Computer Systems and Applications (AICCSA), pp. 1–7 (2010)
Vogiatzis, D., Frosyniotis, D., Papadopoulos, G.A.: The protein structure prediction module of the prot-grid information system. In: Proceedings of ICEIS, pp. 372–378 (2003)
Ward, J.J., Mcguffin, L.J., Buxton, B.F., Jones, D.T.: Secondary structure prediction with support vector machines. Bioinformatics 19, 1650–1655 (2003)
Zemla, A., Vencolvas, C., Fidelis, K., Rost, B.: A modified definition of SOV, a segment-based measure for protein secondary structure prediction assesment. Proteins 34, 220–223 (1999)
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
Hota, C., Ledda, F., Armano, G. (2011). A Resilient Voting Scheme for Improving Secondary Structure Prediction. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-25725-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25724-7
Online ISBN: 978-3-642-25725-4
eBook Packages: Computer ScienceComputer Science (R0)