The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

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

Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

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 …

A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …

Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …

We introduce an ensemble of classifiers-based approach for incremental learning of concept
drift, characterized by nonstationary environments (NSEs), where the underlying data …

Over the last couple of decades, multiple classifier systems, also called ensemble systems
have enjoyed growing attention within the computational intelligence and machine learning …

In this paper, we proposed a novel spam detection method that focused on reducing the
false positive error of mislabeling nonspam as spam. First, we used the wrapper-based …

The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …