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

The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …

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 …

Deep learning techniques have become one of the main propellers for solving engineering
problems effectively and efficiently. For instance, Predictive Maintenance methods have …

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and
reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over …

In this modern age of technology, the Internet of Things has covered all aspects of life
including smart situations, smart homes, and smart spaces. Smart homes have a large …

This book was written having in mind researchers, practitioners, and students willingly to
learn, understand, or perfect the fundamental mechanisms behind intelligence and how they …

Internet reviews significantly influence consumer purchase decisions across all types of
goods and services. However, fake reviews can mislead both customers and businesses …

Dataset shift is a very common issue wherein the input data distribution shifts over time in
non-stationary environments. A broad range of real-world systems face the challenge of …

In real applications learning algorithms have to address several issues such as, huge
amount of data, samples which arrive continuously and underlying data generation …