Skip to main content

Research Repository

Advanced Search

Multi-label classification via label correlation and first order feature dependance in a data stream.

Nguyen, Tien Thanh; Nguyen, Thi Thu Thuy; Luong, Anh Vu; Nguyen, Quoc Viet Hung; Liew, Alan Wee-Chung; Stantic, Bela


Thi Thu Thuy Nguyen

Anh Vu Luong

Quoc Viet Hung Nguyen

Alan Wee-Chung Liew

Bela Stantic


Many batch learning algorithms have been introduced for offline multi-label classification (MLC) over the years. However, the increasing data volume in many applications such as social networks, sensor networks, and traffic monitoring has posed many challenges to batch MLC learning. For example, it is often expensive to re-train the model with the newly arrived samples, or it is impractical to learn on the large volume of data at once. The research on incremental learning is therefore applicable to a large volume of data and especially for data stream. In this study, we develop a Bayesian-based method for learning from multi-label data streams by taking into consideration the correlation between pairs of labels and the relationship between label and feature. In our model, not only the label correlation is learned with each arrived sample with ground truth labels but also the number of predicted labels are adjusted based on Hoeffding inequality and the label cardinality. We also extend the model to handle missing values, a problem common in many real-world data. To handle concept drift, we propose a decay mechanism focusing on the age of the arrived samples to incrementally adapt to the change of data. The experimental results show that our method is highly competitive compared to several well-known benchmark algorithms under both the stationary and concept drift settings. Please note that the published title differs from this accepted manuscript "Multi-label classification via labels correlation and one-dependence features on data stream."


NGUYEN, T.T., NGUYEN, T.T.T., LUONG, A.V., NGUYEN, Q.V.H., LIEW, A.W.-C. and STANTIC, B. 2019. Multi-label classification via label correlation and first order feature dependance in a data stream. Pattern recognition [online], 90, pages 35-51. Available from:

Journal Article Type Article
Acceptance Date Jan 4, 2019
Online Publication Date Jan 6, 2019
Publication Date Jun 30, 2019
Deposit Date Feb 18, 2019
Publicly Available Date Jan 7, 2020
Journal Pattern recognition
Print ISSN 0031-3203
Electronic ISSN 1873-5142
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 90
Pages 35-51
Keywords Multi-label classification; Multi-label learning; Online learning; Data stream; Concept drift; Label correlation; Feature dependence
Public URL


You might also like

Downloadable Citations