Concept learning using one-class classifiers for implicit drift detection in evolving data streams


Data stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate a near-limitless volume of data in temporal order. Such data are referred to as data streams, and are generally nonstationary as the characteristics of data evolves over time. This phe nomenon is called concept drift, and is an issue of great importance in the literature, since it makes models obsolete by decreasing their predictive performance. In the presence of concept drift, it is necessary to adapt to change in data to build more robust and efective classifers. Drift detectors are designed to run jointly with classifcation models, updating them when a signifcant change in data distribution is observed. In this paper, we present an implicit (unsupervised) algorithm called One-Class Drift Detector (OCDD), which uses a one-class learner with a sliding window to detect concept drift. We perform a compre hensive evaluation on mostly recent 17 prevalent concept drift detection methods and an adaptive classifer using 13 datasets. The results show that OCDD outperforms the other methods by producing models with better predictive performance on both real-world and synthetic datasets.

Concept drift, Data stream, Drift detection, Unlabeled data, Verifcation latency