An empirical comparison of methods for multi-label data stream classification

Abstract

This paper studies the problem of multi-label classification in the context of data streams. We discuss related work in this area and present our implementation of several existing approaches as part of the Mulan software. We present empirical results on a real-world data stream concerning media monitoring and discuss and draw a number of conclusions regarding their performance.