18th AIAI 2022, 17 - 20 June 2022, Greece

Experimental comparison of metaheuristics for feature selection in machine learning in the medical context

Thibault Anani, Jean-Fran├žois Pradat-Peyre, Fran├žois Delbot


  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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