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Combined optimization of feature selection and algorithm parameters in machine learning of language

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Abstract
Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the 'right bias' to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons.

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MLA
Daelemans, Walter, et al. “Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language.” Lecture Notes in Artificial Intelligence, edited by N Lavrac et al., vol. 2837, Springer, 2003, pp. 84–95.
APA
Daelemans, W., Hoste, V., De Meulder, F., & Naudts, B. (2003). Combined optimization of feature selection and algorithm parameters in machine learning of language. Lecture Notes in Artificial Intelligence, 2837, 84–95.
Chicago author-date
Daelemans, Walter, Veronique Hoste, Fien De Meulder, and Bart Naudts. 2003. “Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language.” Edited by N Lavrac, D Gamberger, H Blockeel, and L Todorovski. Lecture Notes in Artificial Intelligence 2837: 84–95.
Chicago author-date (all authors)
Daelemans, Walter, Veronique Hoste, Fien De Meulder, and Bart Naudts. 2003. “Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language.” Ed by. N Lavrac, D Gamberger, H Blockeel, and L Todorovski. Lecture Notes in Artificial Intelligence 2837: 84–95.
Vancouver
1.
Daelemans W, Hoste V, De Meulder F, Naudts B. Combined optimization of feature selection and algorithm parameters in machine learning of language. Lavrac N, Gamberger D, Blockeel H, Todorovski L, editors. Lecture Notes in Artificial Intelligence. 2003;2837:84–95.
IEEE
[1]
W. Daelemans, V. Hoste, F. De Meulder, and B. Naudts, “Combined optimization of feature selection and algorithm parameters in machine learning of language,” Lecture Notes in Artificial Intelligence, vol. 2837, pp. 84–95, 2003.
@article{598083,
  abstract     = {Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the 'right bias' to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons.},
  author       = {Daelemans, Walter and Hoste, Veronique and De Meulder, Fien and Naudts, Bart},
  editor       = {Lavrac, N and Gamberger, D and Blockeel, H and Todorovski, L},
  isbn         = {9783540201212},
  issn         = {0302-9743},
  journal      = {Lecture Notes in Artificial Intelligence},
  language     = {eng},
  location     = {Cavtat, Croatia},
  pages        = {84--95},
  publisher    = {Springer},
  title        = {Combined optimization of feature selection and algorithm parameters in machine learning of language},
  url          = {http://dx.doi.org/10.1007/978-3-540-39857-8_10},
  volume       = {2837},
  year         = {2003},
}

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