论文标题
基于稀疏神经网络层的特征选择，并具有标准化的约束
Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints
论文作者
论文摘要
特征选择是机器学习的重要步骤，因为它已显示出可以提高预测准确性，同时降低高维数据维度的诅咒。神经网络在解决许多非线性学习问题方面取得了巨大的成功。在这里，我们提出了新的基于神经网络的特征选择方法，该方法引入了两个约束，其满足导致FS层稀疏。我们已经对合成和现实世界数据进行了广泛的实验，以评估所提出的FS的性能。在实验中，我们专注于高尺寸，低样本量数据，因为这些数据代表了特征选择的主要挑战。结果证实，与其他常规FS方法相比，基于稀疏神经网络层（SNEL-FS）的稀疏神经网络层（SNEL-FS）的提议选择能够选择重要的功能。
Feature selection is important step in machine learning since it has shown to improve prediction accuracy while depressing the curse of dimensionality of high dimensional data. The neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose new neural-network based feature selection approach that introduces two constrains, the satisfying of which leads to sparse FS layer. We have performed extensive experiments on synthetic and real world data to evaluate performance of the proposed FS. In experiments we focus on the high dimension, low sample size data since those represent the main challenge for feature selection. The results confirm that proposed Feature Selection Based on 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.
