Feature Selection Based on a Sparse Neural-Network Layer With Normalizing Constraints
提出一种基于稀疏神经网络层的特征选择方法，通过引入两个约束实现稀疏特征选择，在高维低样本数据上优于传统方法。
Feature selection (FS) is an important step in machine learning since it has been shown to improve prediction accuracy while suppressing the curse of dimensionality of high-dimensional data. Neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose a new neural-network-based FS approach that introduces two constraints, the satisfaction of which leads to a sparse FS layer. We performed extensive experiments on synthetic and real-world data to evaluate the performance of our proposed FS method. In the experiments, we focus on high-dimensional, low-sample-size data since they represent the main challenge for FS. The results confirm that the proposed FS method based on a sparse neural-network layer with normalizing constraints (SNeL-FS) is able to select the important features and yields superior performance compared to other conventional FS methods.