Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

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 …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Unpredictable changes in the underlying distribution of the streaming data over time are
known as concept drift. The development of procedures and techniques for drift detection …

Online reviews have a substantial impact on decision making in various areas of society,
predominantly in the arena of buying and selling of goods. As such, the truthfulness of …

Classification plays a crucial role in mining the evolving data streams. The concept drift and
concept evolution are the major issues of data streams classification, which greatly affect the …

With the development of deep neural networks (DNNs), classifying streaming data with
concept drifts based on DNNs is becoming more and more effective. However, the …

The application of machine learning (ML) in computer systems introduces not only many
benefits but also risks to society. In this paper, we develop the concept of ML governance to …

Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of
massive volumes of data by different applications. It is challenging to apply existing data …

In multi-label learning, data may simultaneously belong to more than one class. When multi-
label data arrives as a stream, the challenges associated with multi-label learning are joined …