New feature selection method based on neural network and machine learning

Nicole Challita (1) , Mohamad Khalil (1) , Pierre Beauseroy (2)

1 Centre AZM pour la Recherche en Biotechnologie et ses Applications
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

Feature selection becomes the focus of much research in many areas of applications for which datasets with large number of features are available. Feature selection is a problem of choosing a subset of relevant features to increase the execution speed of the algorithm and the classification accuracy. It also removes inappropriate features to increase the precision and improve the performances. There has been much effort for solving the feature selection problem up to now and many researchers have proposed and developed many feature selection algorithms in this purpose. In this paper, we propose a new feature selection method based on neural network and machine learning. This new algorithm tends to highlight the best features among existing ones: new weighting-based method of the input features is used in the neural network to choose the best features. Performances show that this method selects the best features on simulated data.

Domains: Computer Science [cs]; Neural and Evolutionary Computing [cs.NE]; Computer Science [cs]; Machine Learning [cs.LG]

Nicole Challita, Mohamad Khalil, Pierre Beauseroy. New feature selection method based on neural network and machine learning. 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), Nov 2016, Beirut, Lebanon. pp.81-85, ⟨10.1109/IMCET.2016.7777431⟩. ⟨hal-02353692⟩