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Algorithmes de selection de variables pour préparer un apprentissage non supervisé. La version finale du programme est sélectionne les prédicteurs les plus pertinents en effectuant un apprentissage à chaque génération. La métrique optimisée (dans le cadre du dataset utilisé) est l'accuracy. Nous avons testé les deux métaheuristiques sur un dataset de 1551 variables. || Two variable selection algorithms to prepare unsupervised learning. The final version of the program selects the variables by performing a learning at each generation. The optimized metric (in the context of the dataset used) is the accuracy rate.

Python 100.00%
differential-evolution genetic-algorithms metaheuristic-algorithms metaheuristic-optimisation unsupervised-clustering

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