LassoLayer: Nonlinear Feature Selection by Switching One-to-one Links
This work addresses the need for nonlinear feature selection in machine learning, offering a domain-specific improvement for tasks with noisy data.
The authors tackled the limitation of Lasso as a linear feature selection method by proposing LassoLayer, a nonlinear approach integrated into neural networks, which outperformed state-of-the-art methods on MNIST dataset tasks.
Along with the desire to address more complex problems, feature selection methods have gained in importance. Feature selection methods can be classified into wrapper method, filter method, and embedded method. Being a powerful embedded feature selection method, Lasso has attracted the attention of many researchers. However, as a linear approach, the applicability of Lasso has been limited. In this work, we propose LassoLayer that is one-to-one connected and trained by L1 optimization, which work to drop out unnecessary units for prediction. For nonlinear feature selections, we build LassoMLP: the network equipped with LassoLayer as its first layer. Because we can insert LassoLayer in any network structure, it can harness the strength of neural network suitable for tasks where feature selection is needed. We evaluate LassoMLP in feature selection with regression and classification tasks. LassoMLP receives features including considerable numbers of noisy factors that is harmful for overfitting. In the experiments using MNIST dataset, we confirm that LassoMLP outperforms the state-of-the-art method.