Nacre: Proactive Recurrent Concept Drift Detection in Data Streams

Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.

I. Introduction

Data streams usually need to be processed and analyzed in real-time, to monitor health conditions, or to prevent adversarial actions such as cyber-attacks and bank fraud. However, data streams tend to evolve over time, resulting in poor and degrading predictive performance in machine learning models. This phenomenon is known as the concept drift [1], [2]. Various techniques have been devised to detect concept drift [3]–[6], so that the model reacts to fit the new distribution. However, there is normally a delay in detection using the existing methodologies. These techniques tend to be reactive, whereby a concept drift is only detected after it occurs. Our research focuses on the problem of proactively predicting and adapting the next drift point timely. The results would allow us to maintain accuracy even when concept drift occurs.