Model selection for support-vector machines through metaheuristic optimization algorithms

Oumeima Ghnimi; Sofiane Kharbech; Akram Belazi; Ammar Bouallegue

doi:10.1117/12.2587439

4 January 2021 Model selection for support-vector machines through metaheuristic optimization algorithms

Oumeima Ghnimi, Sofiane Kharbech, Akram Belazi, Ammar Bouallegue

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160509 (2021) https://doi.org/10.1117/12.2587439
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

A machine learning algorithm aims at designing a mathematical model based on a given training data set. Generally, the built model has a set of parameters that need to be adjusted. Since the performance of a given model depends on its settings, the parameters have to be carefully chosen through a fine-tuning step. A good model selection not only boosts performance but also allows a well-generalized model, i.e., a model that works sound on unseen data. In this paper, we assess the effectiveness of some metaheuristic optimization algorithms for support-vector machines (SVM) model selection. Computer simulations show that optimization algorithms that overall outperforms other algorithms using benchmark functions can be, further, definitely used for an efficient SVM model selection for classification. Thus, we show that Teaching–Learning-Based Optimization algorithm is faster and also enables the most accurate classification, even against other proposed methods in the literature for SVM model selection.

Citation Download Citation

Oumeima Ghnimi, Sofiane Kharbech, Akram Belazi, and Ammar Bouallegue "Model selection for support-vector machines through metaheuristic optimization algorithms", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160509 (4 January 2021); https://doi.org/10.1117/12.2587439

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