Abstract
We introduce common framework for classifiers fusion methods using dynamic weights in decision making process. Both weighted average combiners with dynamic weights and combiners which dynamically estimate local competence are considered. Few algorithms presented in the literature are shown in accordance with our model. In addition we propose two new methods for combining classifiers. The problem of protein secondary structure prediction was selected as a benchmark test. Experiments were carried out on previously prepared dataset of non-homologous proteins for fusion algorithms comparison. The results have proved that developed framework generalizes dynamic weighting approaches and should be further investigated.
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Akkaladevi, S., Balkasim, M., Pan, Y.: Protein Secondary Structure Prediction Using Neural Network and Simulated Annealing Algorithm. In: Proc. 26th Int. Conf. of the IEEE EMBS, pp. 2987–2990. San Francisco (2004)
Aydin, Z., Altunbasak, Y., Borodovsky, M.: Protein Secondary Structure Prediction with Semi Markov HMMS. In: Proc. 26th Int. Conf. of the IEEE EMBS, pp. 2964–2967. San Francisco (2004)
Bermann, H., Westbrock, Z., Feng, G., et al.: The Protein Data Bank. Nucleic Acid Res. 2, 235–242 (2000)
Bridle, J.: Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters. In: Touretzky, D. (ed.) Neural Information Processing Systems, vol. 2, pp. 211–217. Morgan Kaufmann, San Mateo, CA (1990)
Combet, C., Blanchet, C., Geourjon, C., Deleage, G.: NPS@: Network Protein Sequence Analysis. TIBS 25(3), 147–150 (2000)
Garnier, J., Gibrat, J.-F., Robson, B.: GOR secondary structure prediction method version IV. In: Doolittle, R.F. (ed.) Methods in Enzymology, vol. 266, pp. 540–553 (1996)
Geourjon, C., Deleage, G.: SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput. Appl. Biosci. 681–684 (1995)
Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification processes. Image Vision and Computing Journal, 699–707 (2001)
Guermeur, Y.: Combinaison de classifieurs statistiques, Application a la prediction de structure secondaire des proteines, PhD Thesis
Guermeur, Y.: Combining discriminant models with new multi-class SVMs. Pattern Analysis & Applications 5, 168–179 (2002)
Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. PNAS USA, 10915–10919 (1992)
Hobohum, U., Sanders, C.: Enlarged representative set of protein structures. Protein Science 522 (1994)
Jones, D.: Protein Secondary Structure Prediction Based on Position-Specific Scoring Matrices. J. Mol. Biol. 292, 397–407 (1999)
Kabsch, W., Sanders, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 2577–2637 (1983)
Krierger, E., Hooft, R., Nabuurs, S., Vriend, G.: PDBFinderII - a database for protein structure analysis and prediction (2004)
Kuncheva, L.: A theoretical study on six classifier fusion strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 281–286 (2002)
Kuncheva, L.: Combining pattern classifiers: methods and algorithms. John Wiley & Sons, New Jersey (2004)
Selbig, J., Mevissen, T., Lenguaer, T.: Decision-tree Based Formation on Consensus Secondary Structure Prediction. Bioinformatics 15, 1039–1046 (1999)
Wang, L., Liu, J., Zhou, H.: A Comparison of Two Machine Learning Methods for Protein Secondary Structure Prediction. In: Proc. 3rd Int. Conf. on Machine Learning and Cybernetics, pp. 2730–2735. Shanghai (2004)
Xiaochung, Y., Wang, B.: A Protein Secondary Structure Prediction Framework Based on the Support Vector Machine. In: Dong, G., Tang, C.-j., Wang, W. (eds.) WAIM 2003. LNCS, vol. 2762, pp. 266–277. Springer, Heidelberg (2003)
Xu, H., Lau, K., Lu, L.: Protein Secondary Structure Prediction Using Data Mining Tool C5. In: Proc. 11th IEEE Int. Conf. on Tools in AI, pp. 107–110. Chicago (1999)
Zaki, M., Shan, J., Bystroff, C.: Mining Residue Contacts in Protein Using Local Structure Prediction. IEEE Trans. on SMC 33, 258–264 (2003)
Zemla, A., Venclovas, C., Fidelis, K., Rost, B.: Some measures of comparative performance in the tree casps. PROTEINS: Structure, Function, and Genetics, 220–223 (1999)
Zhang, B., Chen, Z., Murphey, Y.: Protein Secondary Structure Prediction Using Machine Learning. In: Proc. IEEE Int. Conf. on Neural Networks, Montreal, pp. 532–537 (2004)
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Woloszynski, T., Kurzynski, M. (2006). Application of Combining Classifiers Using Dynamic Weights to the Protein Secondary Structure Prediction – Comparative Analysis of Fusion Methods. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_8
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DOI: https://doi.org/10.1007/11946465_8
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