Feature selection is an important data preprocessing strategy, has been proven empirically that it contributes to reducing the dimensionality of feature and enhancing the performance of learning algorithms in practice. Typical sparse learning-based models select the features by removing ones that the feature scores are zero. However, linear models puzzle to build the non-linear relations between features and responses. The Deep Neural Network (DNN) has a strong capability to mode the non-linear relations and has been employed to select features. In this paper, we introduce a novel deep Neural network-based Feature Selection (NeuralFS) method to identify features. The new model comprises of a fully-connection network, a decision network, and connect them through a pairwise connected structure. In NeuralFS, the fully-connected network is the crucial structure in NeuralFS that transforms the features into their corresponding scores, and the decision network is the final structure that performs classification or regression. The pairwise connected can be regarded as a “bridge” to connect the two networks, and its weights are fixed as the normalized input as well as it is un-trainable during model training. After optimizing, the feature scores can be obtained by calculating the output of the fully-connected network. NeuralFS takes advantage of the deep network to model the non-linearity, and also make features scores sparse without the sparse regularization technology. We apply the proposed method to both synthetic datasets and benchmark datasets to prove its effectiveness.

特征选择是一种重要的数据预处理策略，已通过经验证明，它有助于减少特征的维数并在实践中提高学习算法的性能。典型的基于稀疏学习的模型通过删除特征得分为零的特征来选择特征。但是，线性模型难以在特征和响应之间建立非线性关系。深度神经网络（DNN）具有对非线性关系进行建模的强大能力，并已被用于选择特征。在本文中，我们介绍了一种新颖的基于深度神经网络的特征选择（NeuralFS）方法来识别特征。新模型包括一个全连接网络，一个决策网络，并通过成对连接的结构将它们连接起来。在NeuralFS中，完全连接的网络是NeuralFS中将要素转换为相应分数的关键结构，而决策网络是执行分类或回归的最终结构。成对连接可以看作是连接两个网络的“桥梁”，其权重固定为标准化输入，并且在模型训练期间是不可训练的。优化后，可以通过计算完全连接的网络的输出来获得特征分数。NeuralFS利用深层网络对非线性进行建模，并且无需稀疏正则化技术即可使特征分数稀疏。我们将提出的方法应用于合成数据集和基准数据集，以证明其有效性。