Feature selection combining genetic algorithm and Adaboost classifiers

Hassan Chouaib (1) , Oriol Ramos Terrades (2) , Salvatore Tabbone (2) , Florence Cloppet (1) , Nicole Vincent (1)

1 CRIP5 - EA 2517 - Centre de Recherche en Informatique de Paris 5
2 QGAR - Querying Graphics through Analysis and Recognition

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Hassan Chouaib, Oriol Ramos Terrades, Salvatore Tabbone, Florence Cloppet, Nicole Vincent. Feature selection combining genetic algorithm and Adaboost classifiers. ICPR 2008 - 19th International Conference on Pattern Recognition, Dec 2008, Tampa-Floride, United States. pp.1-4, ⟨10.1109/ICPR.2008.4761264⟩. ⟨hal-00589401⟩