Marginal Laplacian Score
This work addresses feature selection for imbalanced data in machine learning, but it is incremental as it modifies an existing method.
The paper tackles the challenge of unsupervised feature selection for high-dimensional imbalanced data by introducing the Marginal Laplacian Score (MLS), a modification of the Laplacian Score that focuses on preserving the local structure of the dataset's margin, and integrating it into methods like DUFS-MLS, resulting in robust and improved performance on synthetic and public datasets.
High-dimensional imbalanced data poses a machine learning challenge. In the absence of sufficient or high-quality labels, unsupervised feature selection methods are crucial for the success of subsequent algorithms. Therefore, we introduce a Marginal Laplacian Score (MLS), a modification of the well known Laplacian Score (LS) tailored to better address imbalanced data. We introduce an assumption that the minority class or anomalous appear more frequently in the margin of the features. Consequently, MLS aims to preserve the local structure of the dataset's margin. We propose its integration into modern feature selection methods that utilize the Laplacian score. We integrate the MLS algorithm into the Differentiable Unsupervised Feature Selection (DUFS), resulting in DUFS-MLS. The proposed methods demonstrate robust and improved performance on synthetic and public datasets.