Batch-incremental versus instance-incremental learning in dynamic and evolving data
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
An adaptive ensemble classifier for mining concept drifting data streams
Expert Systems with Applications: An International Journal
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while being ready to predict at any point.This paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and uses it to study the performance of various methods. From this study, we develop a multi-label Hoeffding tree with multi-label classifiers at the leaves. We show empirically that this method is well suited to this challenging task. Using our new framework, which allows us to generate realistic multi-label data streams with concept drift (as well as real data), we compare with a selection of baseline methods, as well as new learning methods from the literature, and show that our Hoeffding tree method achieves fast and more accurate performance.