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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Veröffentlicht in:2008 19th International Conference on Pattern Recognition S. 1 - 4
Hauptverfasser: Chouaib, H., Terrades, O.R., Tabbone, S., Cloppet, F., Vincent, N.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2008
Schlagworte:
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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Zusammenfassung: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.
ISBN:9781424421749
1424421748
ISSN:1051-4651
DOI:10.1109/ICPR.2008.4761264