On Feature Selection Using Anisotropic General Regression Neural Network
This work addresses feature selection for improving machine learning models, but it appears incremental as it adapts an existing neural network with a modified kernel.
The authors tackled the problem of irrelevant features reducing model interpretability and predictive quality by proposing a feature selection method using an anisotropic Gaussian kernel in a General Regression Neural Network, and they demonstrated its robustness and sensitivity to sample size through numerical experiments on simulated data, with comparisons to other methods on real-world datasets.
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.