  Feature selection is generally used as one of the most important
preprocessing techniques in machine learning, as it helps to reduce the
dimensionality of data and assists researchers and practitioners in
understanding data. Thereby, by utilizing feature selection, better performance
and reduced computational consumption, memory complexity and even data amount
can be expected. Although there exist approaches leveraging the power of deep
neural networks to carry out feature selection, many of them often suffer from
sensitive hyperparameters. This paper proposes a feature mask module
(FM-module) for feature selection based on a novel batch-wise attenuation and
feature mask normalization. The proposed method is almost free from
hyperparameters and can be easily integrated into common neural networks as an
embedded feature selection method. Experiments on popular image, text and
speech datasets have shown that our approach is easy to use and has superior
performance in comparison with other state-of-the-art deep-learning-based
feature selection methods.
