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[HTML][HTML] A survey on machine learning for recurring concept drifting data streams
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 …
Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
The so-called “smartization” of manufacturing industries has been conceived as the fourth
industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and …
industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and …
Machine learning with a reject option: A survey
Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …
inaccurate. This behavior should be avoided in many decision support applications, where …
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 …
ElStream: An ensemble learning approach for concept drift detection in dynamic social big data stream learning
With the rapid increase in communication technologies and smart devices, an enormous
surge in data traffic has been observed. A huge amount of data gets generated every …
surge in data traffic has been observed. A huge amount of data gets generated every …
Kappa updated ensemble for drifting data stream mining
Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …
of contemporary machine learning. Algorithms designed for such scenarios must take into …
Reinforcement learning algorithm for non-stationary environments
Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary
environment. However, the stationary assumption on the environment is very restrictive. In …
environment. However, the stationary assumption on the environment is very restrictive. In …
The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony …
As stock data is characterized by highly noisy and non-stationary, stock price prediction is
regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by …
regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by …
No free lunch theorem for concept drift detection in streaming data classification: A review
Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …
are unlabeled because the sheer volume of the stream makes it impractical to label a …
Online ensemble learning algorithm for imbalanced data stream
H Du, Y Zhang, K Gang, L Zhang, YC Chen - Applied Soft Computing, 2021 - Elsevier
In many practical applications, due to the inability to collect complete training data sets at
one time, the adaptability of the classifier is poor. Online ensemble learning can better solve …
one time, the adaptability of the classifier is poor. Online ensemble learning can better solve …