Feature Shift Localization Network

TL;DR

FSL-Net is a neural network designed to quickly and accurately localize feature shifts in large, high-dimensional datasets, aiding in data correction and analysis across various fields.

Contribution

Introduces FSL-Net, a scalable neural network that localizes feature shifts without re-training, handling large and high-dimensional datasets effectively.

Findings

FSL-Net accurately localizes feature shifts in unseen datasets.

The model operates efficiently on high-dimensional data.

Code and trained models are publicly available.

Abstract

Feature shifts between data sources are present in many applications involving healthcare, biomedical, socioeconomic, financial, survey, and multi-sensor data, among others, where unharmonized heterogeneous data sources, noisy data measurements, or inconsistent processing and standardization pipelines can lead to erroneous features. Localizing shifted features is important to address the underlying cause of the shift and correct or filter the data to avoid degrading downstream analysis. While many techniques can detect distribution shifts, localizing the features originating them is still challenging, with current solutions being either inaccurate or not scalable to large and high-dimensional datasets. In this work, we introduce the Feature Shift Localization Network (FSL-Net), a neural network that can localize feature shifts in large and high-dimensional datasets in a fast and accurate manner. The network, trained with a large number of datasets, learns to extract the statistical properties of the datasets and can localize feature shifts from previously unseen datasets and shifts without the need for re-training. The code and ready-to-use trained model are available at https://github.com/AI-sandbox/FSL-Net.