Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition
This work addresses concept drift explanation for online learning systems, particularly in human activity recognition, but it is incremental as it builds on existing feature relevance methods.
The paper tackles the problem of detecting and explaining concept drift in human activity recognition, showing that feature relevance analysis can both detect drift and explain its reason when predefined typical causes are considered, with each cause having a unique effect on feature relevance.
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.