Sparsity-based Feature Selection for Anomalous Subgroup Discovery

TL;DR

This paper introduces SAFS, a scalable, model-agnostic feature selection framework based on sparsity, which improves anomalous subgroup discovery by reducing computation time and enhancing detection performance.

Contribution

The paper presents a novel sparsity-based feature selection method that is scalable, model-agnostic, and improves efficiency and accuracy in anomalous subgroup detection.

Findings

SAFS reduces computation time by over 3 times.

SAFS maintains detection performance comparable to existing methods.

SAFS outperforms multiple baseline feature selection techniques.

Abstract

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.