Collaborative Data Stream Mining In Ubiquitous Environments Using Dynamic Classifier Selection

In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.