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 a synthetic 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. Categor...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Hila Becker, Marta Arias
• Keywords: Concept Drift, Data Mining, KDD 2007, Keywords Concept Drift, Real-world Data Streams