Abstract: Learning from data streams originating from non-stationary environments is vital for many real-world applications. A notable challenge in this task is concept drift. Most existing methods ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

AI (Artificial Intelligence) is a broad concept and its goal is to create intelligent systems whereas Machine Learning is a specific approach to reach the same goal.

Abstract: Concept drift arises from unpredictable data distribution shifts, degrading model performance. In evolving multiple data streams, these drifts pose greater challenges due to dynamic changes ...