Experimental Comparison of Metaheuristics for Feature Selection in Machine Learning in the Medical Context - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2022

Experimental Comparison of Metaheuristics for Feature Selection in Machine Learning in the Medical Context

Abstract

We explore in this paper the use of metaheuristics to select features from a dataset in order to improve the prediction performance of models build with different machine learning methods. To this end, we compare the performances of 5 learning methods: Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM) and Random Forest (RF) on 4 heterogeneous datasets in the number of data and features, for different feature selection methods (metaheuristics or statistical filters).The results obtained show that feature selection by improving a metaheuristic derived from the genetic algorithm leads to much better performances no matter the learning method used compared to without feature selection on the same dataset.
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hal-04668638 , version 1 (07-08-2024)

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Thibault Anani, Francois Delbot, Jean-François Pradat-Peyre. Experimental Comparison of Metaheuristics for Feature Selection in Machine Learning in the Medical Context. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.194-205, ⟨10.1007/978-3-031-08337-2_17⟩. ⟨hal-04668638⟩
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