Ammar Shaker

TL;DR: The main methodological concepts underlying this approach to learning on data streams are introduced and its implementation under the MOA software framework is discussed and it turns out to be competitive to state-of-the-art methods in terms of prediction accuracy.

TL;DR: The new approach foresees an early drift recognition variant, which relies on divergence measures, indicating the degree of divergence in local parts of the feature space separately already before the global model error may start to rise significantly, which can be seen as an attempt regarding drift prevention on global model level.

TL;DR: In this article, an evolving version of Fuzzy pattern trees (FPTs) is proposed for binary classification from data streams, in which model adaptation is realized by anticipating possible local changes of the current model, and confirming these changes through statistical hypothesis testing.

TL;DR: This paper advocates an instance-based learning algorithm for that purpose, both for classification and regression problems, that has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives.