< jats: p> The primary task of machine learning is to extract valuable information from the
data that is generated every day, process it to learn from it, and take useful actions. Original …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Abstract New York City taxi rides form the core of the traffic in the city of New York. The many
rides taken every day by New Yorkers in the busy city can give us a great idea of traffic …

The recent emergence of cryptocurrencies has added another layer of complexity in the fight
towards financial crime. Cryptocurrencies require no central authority and offer pseudo …

At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

As machine learning models increasingly replace traditional business logic in the production
system, their lifecycle management is becoming a significant concern. Once deployed into …

Gradient Boosting is a widely-used machine learning technique that has proven highly
effective in batch learning. However, its effectiveness in stream learning contexts lags …

Data streams can be defined as the continuous stream of data coming from different sources
and in different forms. Streams are often very dynamic, and its underlying structure usually …

Artificial intelligence (AI)-based decision-making systems are employed nowadays in an
ever growing number of online as well as offline services-some of great importance …

Automated indoor environmental control is a research topic that is beginning to receive
much attention in smart home automation. All machine learning models proposed to date for …