Multistream Classification

Multistream classification tackles the challenge of analyzing and classifying data from multiple, potentially related, streams simultaneously. Current research focuses on adapting to dynamic data streams with concept drift, often employing online boosting algorithms or deep clustering networks with adversarial domain adaptation techniques to handle variations in data distribution across streams and limited labeled data. These methods aim to improve prediction accuracy and robustness by leveraging correlations between streams and mitigating negative transfer from irrelevant data. The advancements in this field have significant implications for various applications, including real-time monitoring systems and personalized recommendations, where data arrives from multiple sources with varying characteristics.