| Title: |
Dealing with Concept Drift and Class Imbalance in Multi-label Stream Classification |
| Author(s): |
E. Spyromitros-Xioufis, M. Spiliopoulou, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (6 pages).
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| Keywords: |
Multi-label Classification, Concept Drift, Class Imbalance, Multiple Windows.
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| Appeared in: |
Proc. 22nd International Conference on Artificial Intelligence (IJCAI 2011), AAAI press, Barcelona, Spain, 2011.
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| Abstract: |
Streams of objects that are associated with one or more labels at the same time appear in many applications. However, stream classification of multi-label data is largely unexplored.
Existing approaches try to tackle the problem by transferring traditional single-label stream classification practices to the multi-label domain. Nevertheless, they fail to consider some of the unique properties of the problem such as within and between class imbalance and multiple concept drift. To deal with these challenges, this paper proposes a novel multi-label stream classification approach that employs two windows for each label, one for positive and one for negative examples. Instance-sharing is exploited for space efficiency, while a time-efficient instantiation based on the k-Nearest Neighbor algorithm is also proposed. Finally, a batch-incremental thresholding technique is proposed to further deal with the class imbalance problem. Results of an empirical comparison against two other methods on three real world datasets are in favor of the proposed approach. |
| See also : |
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This paper has been cited by the following:
| 1 |
Kong, X., Yu, P.S. (2011) An ensemble-based approach to fast classification of multi-label data streams, ColiaborateCom 2011 - Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, art. no. 6144793, pp. 95-104.
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| 2 |
Nguyen, Hien M.; Cooper, Eric W.; Kamei, Katsuari (2011) Online learning from imbalanced data streams, Proc. 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp 347 - 352
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| 3 |
Wang,P.;Zhang,P.;Guo,L. (2012) Mining Multi-label Data Streams Using Ensemble-based Active Learning, In Proceedings of the 2012 SIAM International Conference on Data Mining (SDM-12), pp. 1131-1140, Anaheim, California, USA. |
| 4 |
Nishida, Y., Hoshide, T., Fujimura, K. (2012) Improving tweet stream classification by detecting changes in word probability, In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '12), pp 971-980.
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| 5 |
Ditzler,G.;Polikar,R. (2012) Incremental Learning of Concept Drift from Streaming Imbalanced Data, IEEE Transactions on Knowledge and Data Engineering, 05 July 2012. |
| 6 |
Read, J., Bifet, A., Holmes, G., Pfahringer, B. (2012) Scalable and efficient multi-label classification for evolving data streams, Machine Learning, 88 (1-2), pp. 243-272. |
| 7 |
Nguyen, H.M., Cooper, E.W., Kamei, K. (2012) Adaptive data reuse for classifying imbalanced and concept-drifting data streams, International Journal of Innovative Computing, Information and Control, 8 (7 B), pp. 4995-5010. |
| 8 |
Nguyen, H.M., Cooper, E.W., Kamei, K. (2012) Mining imbalanced and concept-drifting data streams using support vector machines, ICIC Express Letters, 6 (2), pp. 455-460. |
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