Handling Concept Drift via Model Reuse

TL;DR

This paper introduces a novel approach to handle concept drift in data streams by reusing models and adaptively adjusting their weights based on performance, validated through theoretical analysis and experiments.

Contribution

The paper proposes a new model reuse method for concept drift that adaptively weights previous models, with theoretical guarantees and superior experimental results.

Findings

Effective handling of concept drift demonstrated on synthetic and real datasets.

The approach outperforms existing methods in accuracy and adaptability.

Theoretical analysis confirms generalization and regret bounds.

Abstract

In many real-world applications, data are often collected in the form of stream, and thus the distribution usually changes in nature, which is referred as concept drift in literature. We propose a novel and effective approach to handle concept drift via model reuse, leveraging previous knowledge by reusing models. Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the model performance. We provide generalization and regret analysis. Experimental results also validate the superiority of our approach on both synthetic and real-world datasets.