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 …

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are
impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI …

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 …

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 …

A challenge to many real-world applications is multiclass imbalance with concept drift. In this
paper, we propose a comprehensive active learning method for multiclass imbalanced …

With the rapid development of ambient intelligence (AmI) in the Internet of Things (IoT),
many data streams are generated from sensing devices in intelligent scenarios. Due to the …

The complex problems of multiclass imbalance, virtual or real concept drift, concept
evolution, high-speed traffic streams and limited label cost budgets pose severe challenges …

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

In stream learning, data continuously arrives over time, often at a very high rate. For
imbalanced data streams with concept drift, it becomes essential to simultaneously address …

One of the most challenging problems in the field of online learning is concept drift, which
deeply influences the classification stability of streaming data. If the data stream is …