Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition

TL;DR

This paper presents a method for detecting and explaining concept drift in human activity recognition by analyzing feature relevance changes, enabling both drift detection and understanding of its causes.

Contribution

It introduces a novel approach that uses feature relevance analysis to detect and explain concept drift, linking specific relevance changes to predefined drift reasons.

Findings

Feature relevance analysis can detect concept drift effectively.

Relevance changes are uniquely associated with specific drift reasons.

Method improves interpretability of drift detection in human activity recognition.

Abstract

This article studies how to detect and explain concept drift. Human activity recognition is used as a case study together with a online batch learning situation where the quality of the labels used in the model updating process starts to decrease. Drift detection is based on identifying a set of features having the largest relevance difference between the drifting model and a model that is known to be accurate and monitoring how the relevance of these features changes over time. As a main result of this article, it is shown that feature relevance analysis cannot only be used to detect the concept drift but also to explain the reason for the drift when a limited number of typical reasons for the concept drift are predefined. To explain the reason for the concept drift, it is studied how these predefined reasons effect to feature relevance. In fact, it is shown that each of these has an unique effect to features relevance and these can be used to explain the reason for concept drift.