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Digging into acceptor splice site prediction : an iterative feature selection approach

Yvan Saeys (UGent) , Sven Degroeve (UGent) and Yves Van de Peer (UGent)
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Abstract
Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets.
Keywords
CLASSIFICATION, ALGORITHMS

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Chicago
Saeys, Yvan, Sven Degroeve, and Yves Van de Peer. 2004. “Digging into Acceptor Splice Site Prediction : an Iterative Feature Selection Approach.” Ed. Jean-François Boulicaut, Floriana Esposito, Fosca Giannotti, and Dino Pedreschi. Lecture Notes in Artificial Intelligence 3202: 386–397.
APA
Saeys, Yvan, Degroeve, S., & Van de Peer, Y. (2004). Digging into acceptor splice site prediction : an iterative feature selection approach. (J.-F. Boulicaut, F. Esposito, F. Giannotti, & D. Pedreschi, Eds.)LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 3202, 386–397. Presented at the 8th European conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2004).
Vancouver
1.
Saeys Y, Degroeve S, Van de Peer Y. Digging into acceptor splice site prediction : an iterative feature selection approach. Boulicaut J-F, Esposito F, Giannotti F, Pedreschi D, editors. LECTURE NOTES IN ARTIFICIAL INTELLIGENCE. Berlin, Germany: Springer; 2004;3202:386–97.
MLA
Saeys, Yvan, Sven Degroeve, and Yves Van de Peer. “Digging into Acceptor Splice Site Prediction : an Iterative Feature Selection Approach.” Ed. Jean-François Boulicaut et al. LECTURE NOTES IN ARTIFICIAL INTELLIGENCE 3202 (2004): 386–397. Print.
@article{297231,
  abstract     = {Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction.
We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature.
The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets.},
  author       = {Saeys, Yvan and Degroeve, Sven and Van de Peer, Yves},
  editor       = {Boulicaut, Jean-François and Esposito, Floriana and Giannotti, Fosca and Pedreschi, Dino},
  isbn         = {9783540231080},
  issn         = {0302-9743},
  journal      = {LECTURE NOTES IN ARTIFICIAL INTELLIGENCE},
  keywords     = {CLASSIFICATION,ALGORITHMS},
  language     = {eng},
  location     = {Pisa, Italy},
  pages        = {386--397},
  publisher    = {Springer},
  title        = {Digging into acceptor splice site prediction : an iterative feature selection approach},
  url          = {http://dx.doi.org/10.1007/978-3-540-30116-5_36},
  volume       = {3202},
  year         = {2004},
}

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