Sparsity-based Feature Selection for Anomalous Subgroup Discovery
This addresses the problem of inefficient feature selection for anomalous subgroup discovery, particularly in domains like critical care, though it appears incremental as it builds on existing detection techniques.
The paper tackles the lack of principled and scalable feature selection for anomalous pattern detection by proposing a sparsity-based automated feature selection (SAFS) framework, which achieves over 3x reduction in computation time while maintaining detection performance on a critical care dataset.
Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques have been proposed in the state of the art. However, there is a common lack of a principled and scalable feature selection method for efficient discovery. Existing feature selection techniques are often conducted by optimizing the performance of prediction outcomes rather than its systemic deviations from the expected. In this paper, we proposed a sparsity-based automated feature selection (SAFS) framework, which encodes systemic outcome deviations via the sparsity of feature-driven odds ratios. SAFS is a model-agnostic approach with usability across different discovery techniques. SAFS achieves more than $3\times$ reduction in computation time while maintaining detection performance when validated on publicly available critical care dataset. SAFS also results in a superior performance when compared against multiple baselines for feature selection.