[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 …
Learning under concept drift: A review
Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …
data overtime. Concept drift research involves the development of methodologies 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 …
[HTML][HTML] A recent overview of the state-of-the-art elements of text classification
MM Mirończuk, J Protasiewicz - Expert Systems with Applications, 2018 - Elsevier
The aim of this study is to provide an overview the state-of-the-art elements of text
classification. For this purpose, we first select and investigate the primary and recent studies …
classification. For this purpose, we first select and investigate the primary and recent studies …
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 …
A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection
Today's internet data primarily consists of streamed data from various applications like
sensor networks, banking data and telecommunication data networks. A new field of study …
sensor networks, banking data and telecommunication data networks. A new field of study …
[HTML][HTML] A survey on machine learning for recurring concept drifting data streams
AL Suárez-Cetrulo, D Quintana, A Cervantes - Expert Systems with …, 2023 - Elsevier
The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
[HTML][HTML] On evaluating stream learning algorithms
Most streaming decision models evolve continuously over time, run in resource-aware
environments, and detect and react to changes in the environment generating data. One …
environments, and detect and react to changes in the environment generating data. One …
Federated learning under distributed concept drift
Federated Learning (FL) under distributed concept drift is a largely unexplored area.
Although concept drift is itself a well-studied phenomenon, it poses particular challenges for …
Although concept drift is itself a well-studied phenomenon, it poses particular challenges for …
On the reliable detection of concept drift from streaming unlabeled data
TS Sethi, M Kantardzic - Expert Systems with Applications, 2017 - Elsevier
Classifiers deployed in the real world operate in a dynamic environment, where the data
distribution can change over time. These changes, referred to as concept drift, can cause the …
distribution can change over time. These changes, referred to as concept drift, can cause the …