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Communication Dans Un Congrès Année : 2021

Model selection for support-vector machines through metaheuristic optimization algorithms

Résumé

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.
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Dates et versions

hal-03449833 , version 1 (25-11-2021)

Identifiants

Citer

Oumeima Ghnimi, Sofiane Kharbech, Akram Belazi, Ammar Bouallegue. Model selection for support-vector machines through metaheuristic optimization algorithms. Thirteenth International Conference on Machine Vision, ICMV 2020, Session 6 - Artificial Intelligence and Intelligent Computing, SPIE, Nov 2020, Rome, Italy. pp.1160509, 59, ⟨10.1117/12.2587439⟩. ⟨hal-03449833⟩
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