Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

Real-Time Concept Drift Detection and Its Application to ECG Data

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world & time-series datasets.

Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

The embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.