document: Real-time ranking with concept drift using expert advice In many practical applications , one is interested in generating a ranked list of items using information mined from continuous streams of data . For example , in the context of computer networks , one might want to generate lists of nodes ranked according to their susceptibility to attack . In addition , real-world data streams often exhibit concept drift , making the learning task even more challenging . We present an online learning approach to ranking with concept drift , using weighted majority techniques . By continuously modeling different snapshots of the data and tuning our measure of belief in these models over time , we capture changes in the underlying concept and adapt our predictions accordingly . We measure the performance of our algorithm on real electricity data as well as asynthetic data stream , and demonstrate that our approach to ranking from stream data outperforms previously known batch-learning methods and other online methods that do not account for concept drift .
doc_bio_tags: O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O B O O O O O O O O O B O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O