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A bayesian nonparametric approach for multi-label classification

Nguyen, Vu, Gupta, Sunil, Rana, Santu, Li, Cheng and Venkatesh, Svetha 2016, A bayesian nonparametric approach for multi-label classification, in ACML 2016: Proceedings of the 8th Asian Conference on Machine Learning, [The Conference], [Hamilton, New Zealand], pp. 254-269.

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Title A bayesian nonparametric approach for multi-label classification
Author(s) Nguyen, Vu
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Li, Cheng
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Asian Conference in Machine Learning (8th : 2016 : Hamilton, New Zealand)
Conference location Hamilton, New Zealand
Conference dates 16-18 Nov. 2016
Title of proceedings ACML 2016: Proceedings of the 8th Asian Conference on Machine Learning
Editor(s) Durrant, Robert J.
Kim, Kee-Eung
Publication date 2016
Conference series Asian Conference in Machine Learning
Start page 254
End page 269
Total pages 16
Publisher [The Conference]
Place of publication [Hamilton, New Zealand]
Summary Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for prediction. Traditional classification methods have attempted to reveal these correlations in different ways. However, existing methods demand expensive computation complexity for finding such correlation structures. Furthermore, these approaches can not identify the suitable number of label-feature correlation patterns. In this paper, we propose a Bayesian nonparametric (BNP) framework for multi-label classification that can automatically learn and exploit the unknown number of multi-label correlation. We utilize the recent techniques in stochastic inference to derive the cheap (but efficient) posterior inference algorithm for the model. In addition, our model can naturally exploit the useful information from missing label samples. Furthermore, we extend the model to update parameters in an online fashion that highlights the flexibility of our model against the existing approaches. We compare our method with the state-of-the-art multi-label classification algorithms on real-world datasets using both complete and missing label settings. Our model achieves better classification accuracy while our running time is consistently much faster than the baselines in an order of magnitude.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30094583

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