Detecting group concept drift from multiple data streams
Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …
based algorithms, caused by unforeseeable changes in the underlying distribution of data …
Dynamic submodular-based learning strategy in imbalanced drifting streams for real-time safety assessment in nonstationary environments
The design of real-time safety assessment (RTSA) approaches in nonstationary
environments is meaningful to reduce the possibility of significant losses. However, several …
environments is meaningful to reduce the possibility of significant losses. However, several …
Cost-sensitive continuous ensemble kernel learning for imbalanced data streams with concept drift
Y Chen, X Yang, HL Dai - Knowledge-Based Systems, 2024 - Elsevier
In stream learning, data continuously arrives over time, often at a very high rate. For
imbalanced data streams with concept drift, it becomes essential to simultaneously address …
imbalanced data streams with concept drift, it becomes essential to simultaneously address …
Predicting wettability of mineral/CO2/brine systems via data-driven machine learning modeling: Implications for carbon geo-sequestration
Effectively storing carbon dioxide (CO 2) in geological formations synergizes with algal-
based removal technology, enhancing carbon capture efficiency, leveraging biological …
based removal technology, enhancing carbon capture efficiency, leveraging biological …
CDSS for early recognition of respiratory diseases based on AI techniques: a systematic review
Respiratory diseases such as Asthma, COVID-19, etc., require preventive and precautionary
measures. Due to the lack of medical treatment for the masses, researchers are currently …
measures. Due to the lack of medical treatment for the masses, researchers are currently …
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams
Existing concept drift adaptation (CDA) methods aim to continually update outdated
classifiers in a single-labeled stream scenario. However, real-world data streams are …
classifiers in a single-labeled stream scenario. However, real-world data streams are …
Concept drift adaptation by exploiting drift type
Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …
When this happens, model predictions become less accurate. Hence, models built in the …
Evidential ensemble preference-guided learning approach for real-time multimode fault diagnosis
Operational changes in industrial production can alter system operating modes, which
complicates real-time fault diagnosis by affecting sensor data and fault characteristics. In …
complicates real-time fault diagnosis by affecting sensor data and fault characteristics. In …
An Experimental Study and Machine Learning Modeling of Shale Swelling in Extended Reach Wells When Exposed to Diverse Water-Based Drilling Fluids
Shale swelling poses considerable challenges for companies involved in extended-reach
well drilling, particularly when it comes to maintaining wellbore stability. Despite the …
well drilling, particularly when it comes to maintaining wellbore stability. Despite the …
A self-adaptive ensemble for user interest drift learning
User interest reflects user preference which plays an important role in commercial decision-
making. Learning and predicting user interest has attracted significant attention in recent …
making. Learning and predicting user interest has attracted significant attention in recent …