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 …

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 …

Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …

Facial image manipulation is a particular instance of digital image tampering, which is done
by compositing a region from one facial image into another facial image. Fake images …

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 …

We have witnessed in recent years an ever-growing volume of information becoming
available in a streaming manner in various application areas. As a result, there is an …

As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been
tested extensively with static data. However, real-world applications often involve dynamic …

Learning nonstationary data streams has been well studied in recent years. However, most
of the researches assume that the class imbalance of data streams is relatively balanced …

Inventory backorder prediction is widely recognized as an important component of inventory
models. However, backorder prediction is traditionally based on stochastic approximation …

Class imbalance introduces additional challenges when learning classifiers from concept
drifting data streams. Most existing work focuses on designing new algorithms for dealing …