Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

Many daily applications are generating massive amount of data in the form of stream at an
ever higher speed, such as medical data, clicking stream, internet record and banking …

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 …

Phishing is an online threat where an attacker impersonates an authentic and trustworthy
organization to obtain sensitive information from a victim. One example of such is trolling …

Training and testing process for the classification of biomedical datasets in machine learning
is very important. The researcher should choose carefully the methods that should be used …

Classification methods for streaming data are not new, but very few current frameworks
address all three of the most common problems with these tasks: concept drift, noise, and …

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 …

The features of the kernel extreme learning machine—efficient processing, improved
performance, and less human parameter setting—have allowed it to be effectively used to …

Fire is a natural disaster that can be caused by many different reasons. Recently, more
environmentally friendly and innovative extinguishing methods have started to be tested …

One of the most critical data analysis tasks is the streaming data classification, where we
may also observe the concept drift phenomenon, ie, changing the decision model's …