Feature selection combining genetic algorithm and Adaboost classifiers

This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rat...

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Vydáno v:2008 19th International Conference on Pattern Recognition s. 1 - 4
Hlavní autoři: Chouaib, H., Terrades, O.R., Tabbone, S., Cloppet, F., Vincent, N.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2008
Témata:
Biological cells
Costs
Diversity reception
Electronic mail
Filters
Genetic algorithms
Machine learning
Neural networks
Pattern recognition
Spatial databases
ISBN:9781424421749, 1424421748
ISSN:1051-4651
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Abstract This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.
AbstractList This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.
Author Chouaib, H.
Tabbone, S.
Cloppet, F.
Terrades, O.R.
Vincent, N.
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  surname: Chouaib
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  organization: Lab. CRIP5, Univ. Paris Descartes, Paris, France
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  givenname: O.R.
  surname: Terrades
  fullname: Terrades, O.R.
  organization: LORIA, Univ. Nancy 2, Nancy, France
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  givenname: S.
  surname: Tabbone
  fullname: Tabbone, S.
  organization: LORIA, Univ. Nancy 2, Nancy, France
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  surname: Cloppet
  fullname: Cloppet, F.
  organization: Lab. CRIP5, Univ. Paris Descartes, Paris, France
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  givenname: N.
  surname: Vincent
  fullname: Vincent, N.
  organization: Lab. CRIP5, Univ. Paris Descartes, Paris, France
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Snippet This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the...
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SubjectTerms Biological cells
Costs
Diversity reception
Electronic mail
Filters
Genetic algorithms
Machine learning
Neural networks
Pattern recognition
Spatial databases
Title Feature selection combining genetic algorithm and Adaboost classifiers
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