Combining Protein Secondary Structure Prediction Models with Ensemble Methods of Optimal Complexity

Yann Guermeur 1 Dominique Zelus
1 MODBIO - Computational models in molecular biology
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The idea of combining models instead of simply selecting the ``best'' one, in order to improve performance, has a long theoretical background in statistics. However, theoretical results are ordinarily based on strong hypotheses, seldom satisfied in practice. When dealing with real-world problems, overfitting is often the main limitation, which cannot be overcome but with a strict complexity control of the combiner selected. SVMs should thus be well suited for these difficult situations. Investigating this idea, we introduce a new family of multi-class SVMs, and assess them as ensemble methods for protein secondary structure prediction. Experimental evidence highlights the gain in prediction accuracy resulting from combining some of the current best prediction methods with our SVMs rather than with the combiners traditionally used in the field.
Type de document :
Communication dans un congrès
Laurent Duret. Journées Ouvertes Biologie Informatique Mathématiques - JOBIM'2001, 2001, Toulouse, France, pp.97-104, 2001
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Soumis le : mardi 26 septembre 2006 - 14:56:27
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

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  • HAL Id : inria-00101092, version 1

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Yann Guermeur, Dominique Zelus. Combining Protein Secondary Structure Prediction Models with Ensemble Methods of Optimal Complexity. Laurent Duret. Journées Ouvertes Biologie Informatique Mathématiques - JOBIM'2001, 2001, Toulouse, France, pp.97-104, 2001. 〈inria-00101092〉

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