Abstract
Image features selection consists on reducing the number of features of an image, used for training or testing a classifier, by eliminating irrelevant, noisy and redundant data without decreasing significantly the prediction accuracy of the classifier. In this paper we consider the problem of feature selection. We give a quick technique dependent on a binary Genetic Algorithm (GA) combined with Neighboring Support Vector Classifier NSVC. More concretely, we utilize the k-Nearest Neighbors KNN classifier as a fitness function for each feature selection decision during the evaluation step of the genetic algorithm. GA-NSVC is implemented and tested on BOSS and MIT-CBCL Face dataset. The results show its robustness and high performances.
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Ngadi, M., Amine, A., Nassih, B., Azdoud, Y., El-Attar, A. (2021). A Robust Method for Face Classification Based on Binary Genetic Algorithm Combined with NSVC Classifier. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_5
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DOI: https://doi.org/10.1007/978-3-030-73882-2_5
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