We have witnessed in recent years an ever-growing volume of information becoming
available in a streaming manner in various application areas. As a result, there is an …

Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …

In this paper, we propose MUSE-RNN, a multilayer self-evolving recurrent neural network
model for real-time classification of streaming data. Unlike the existing approaches, MUSE …

Data streaming classification has become an essential task in many fields where real-time
decisions have to be made based on incoming information. Neural networks are a …

There is an emerging need for predictive models to be trained on-the-fly, since in numerous
machine learning applications data are arriving in an online fashion. A critical challenge …

In a streaming environment, the characteristics and labels of instances may change over
time, forming concept drifts. Previous studies on data stream learning generally assume that …

In recent years Deep Learning based methods gained a growing recognition in many
applications and became the state-of-the-art approach in various fields of Machine Learning …

Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

Supervised data stream learning depends on the incoming sample's true label to update a
classifier's model. In real life, obtaining the ground truth for each instance is a challenging …

This work presents SAE2, a dynamic ensemble classifier for data stream classification that is
built on the Social Adaptive Ensemble (SAE). Similarly to its predecessor, SAE2 maintains …