Streaming Multi-label Classification

Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer
Proceedings of the Second Workshop on Applications of Pattern Analysis, PMLR 17:19-25, 2011.

Abstract

This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as 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 the learners must be able to adapt to change using limited time and memory. We present a new experimental software that extends the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. It is released under the GNU GPL license.

Cite this Paper

BibTeX
@InProceedings{pmlr-v17-read11a, title = {Streaming Multi-label Classification}, author = {Read, Jesse and Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard}, booktitle = {Proceedings of the Second Workshop on Applications of Pattern Analysis}, pages = {19--25}, year = {2011}, editor = {Diethe, Tom and Balcazar, Jose and Shawe-Taylor, John and Tirnauca, Cristina}, volume = {17}, series = {Proceedings of Machine Learning Research}, address = {CIEM, Castro Urdiales, Spain}, month = {19--21 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v17/read11a/read11a.pdf}, url = {https://proceedings.mlr.press/v17/read11a.html}, abstract = {This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as 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 the learners must be able to adapt to change using limited time and memory. We present a new experimental software that extends the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. It is released under the GNU GPL license.} }
Endnote
%0 Conference Paper %T Streaming Multi-label Classification %A Jesse Read %A Albert Bifet %A Geoff Holmes %A Bernhard Pfahringer %B Proceedings of the Second Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2011 %E Tom Diethe %E Jose Balcazar %E John Shawe-Taylor %E Cristina Tirnauca %F pmlr-v17-read11a %I PMLR %P 19--25 %U https://proceedings.mlr.press/v17/read11a.html %V 17 %X This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as 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 the learners must be able to adapt to change using limited time and memory. We present a new experimental software that extends the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. It is released under the GNU GPL license.
RIS
TY - CPAPER TI - Streaming Multi-label Classification AU - Jesse Read AU - Albert Bifet AU - Geoff Holmes AU - Bernhard Pfahringer BT - Proceedings of the Second Workshop on Applications of Pattern Analysis DA - 2011/10/21 ED - Tom Diethe ED - Jose Balcazar ED - John Shawe-Taylor ED - Cristina Tirnauca ID - pmlr-v17-read11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 17 SP - 19 EP - 25 L1 - http://proceedings.mlr.press/v17/read11a/read11a.pdf UR - https://proceedings.mlr.press/v17/read11a.html AB - This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as 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 the learners must be able to adapt to change using limited time and memory. We present a new experimental software that extends the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. It is released under the GNU GPL license. ER -
APA
Read, J., Bifet, A., Holmes, G. & Pfahringer, B.. (2011). Streaming Multi-label Classification. Proceedings of the Second Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 17:19-25 Available from https://proceedings.mlr.press/v17/read11a.html.

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