Data Stream Classification

Data stream classification focuses on building machine learning models that can accurately classify data arriving continuously and potentially indefinitely, often exhibiting concept drift (changing data distributions) and class imbalance. Current research emphasizes adapting existing algorithms like k-NN and decision trees, and exploring novel architectures such as convolutional neural networks applied to multi-dimensional data encodings, and ensemble methods incorporating active learning and dynamic selection to handle these challenges. This field is crucial for real-world applications processing continuous data streams, such as fraud detection, social media monitoring, and autonomous systems, where efficient and robust classification in dynamic environments is paramount.