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

Abstract

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."

Citation

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: https://doi.org/10.1016/j.patcog.2019.01.007

• 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
• DOI: https://doi.org/10.1016/j.patcog.2019.01.007
• Keywords: Multi-label classification; Multi-label learning; Online learning; Data stream; Concept drift; Label correlation; Feature dependence
• Public URL: http://hdl.handle.net/10059/3301