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 …
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
Network intrusion detection combined hybrid sampling with deep hierarchical network
K Jiang, W Wang, A Wang, H Wu - IEEE access, 2020 - ieeexplore.ieee.org
Intrusion detection system (IDS) plays an important role in network security by discovering
and preventing malicious activities. Due to the complex and time-varying network …
and preventing malicious activities. Due to the complex and time-varying network …
Data-driven evolutionary optimization: An overview and case studies
Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …
constraint functions is straightforward. In solving many real-world optimization problems …
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …
algorithms recently proposed in the literature tackle this problem using a variety of data …
Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
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 …
[HTML][HTML] Learning from imbalanced data: open challenges and future directions
B Krawczyk - Progress in artificial intelligence, 2016 - Springer
Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …
is still a focus of intense research. Starting as a problem of skewed distributions of binary …
Effective data generation for imbalanced learning using conditional generative adversarial networks
Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …
classification algorithms. Although there are different strategies to address this problem …
Credit card fraud detection: a realistic modeling and a novel learning strategy
Detecting frauds in credit card transactions is perhaps one of the best testbeds for
computational intelligence algorithms. In fact, this problem involves a number of relevant …
computational intelligence algorithms. In fact, this problem involves a number of relevant …