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[HTML][HTML] From concept drift to model degradation: An overview on performance-aware drift detectors
The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …
machine learning (ML) models. Changes in the system on which the ML model has been …
Ensemble learning: A survey
Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …
challenges. Such methods improve the predictive performance of a single model by training …
Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects
The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …
(ML) have affected several research fields, leading to improvements that could not have …
Ensemble learning for data stream analysis: A survey
In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …
environments where data are collected in the form of transient data streams. Compared to …
Adaptive random forests for evolving data stream classification
Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …
streaming (batch) setting. This preference is attributable to its high learning performance and …
A survey on ensemble learning for data stream classification
Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …
classification. Their popularity is attributable to their good performance in comparison to …
A survey on data preprocessing for data stream mining: Current status and future directions
Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …
discovery scenarios, dominated by increasingly large datasets. These methods aim at …
Learning in nonstationary environments: A survey
The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …
sensors has led to an enormous and ever increasing amount of data that are now more …
A survey on concept drift adaptation
Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …
between the input data and the target variable changes over time. Assuming a general …
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
Data streams are potentially unbounded sequences of instances arriving over time to a
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …