论文标题
使用各向异性通用回归神经网络进行特征选择
On Feature Selection Using Anisotropic General Regression Neural Network
论文作者
论文摘要
输入数据集中存在无关的特征倾向于降低机器学习模型的可解释性和预测质量。因此，开发特征选择方法以识别无关紧要的特征是机器学习中的关键主题。在这里，我们展示了如何使用各向异性高斯内核用于执行特征选择的通用回归神经网络。使用模拟数据进行了许多数值实验，以研究所提出的方法的鲁棒性及其对样本量的敏感性。最后，在几个现实世界数据集上进行了与其他四种功能选择方法的比较。
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of feature selection methods to recognize irrelevant features is a crucial topic in machine learning. Here we show how the General Regression Neural Network used with an anisotropic Gaussian Kernel can be used to perform feature selection. A number of numerical experiments are conducted using simulated data to study the robustness of the proposed methodology and its sensitivity to sample size. Finally, a comparison with four other feature selection methods is performed on several real world datasets.
