# Drift Estimation with Graphical Models

arXiv: https://arxiv.org/abs/2102.01458 · HTML: https://mlsanity.com/paper/2102.01458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)

## Abstract (arXiv)

This paper deals with the issue of concept drift in supervised machine learn-ing. We make use of graphical models to elicit the visible structure of the dataand we infer from there changes in the hidden context. Differently from previous concept-drift detection methods, this application does not depend on the supervised machine learning model in use for a specific target variable, but it tries to assess the concept drift as independent characteristic of the evolution of a dataset. Specifically, we investigate how a graphical model evolves by looking at the creation of new links and the disappearing of existing ones in different time periods. The paper suggests a method that highlights the changes and eventually produce a metric to evaluate the stability over time. The paper evaluate the method with real world data on the Australian Electric market.