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

作者：

Highlights：

• A Bayesian-based multi-label online learning method for multi-label data stream classification is proposed.

• Our method not only learns the label correlation from each arrived sample but also dynamically determines the number of predicted labels based on Hoeffding inequality and the label cardinality.

• Our method can also handle missing values and concept drifts in the data stream effectively.

• Extensive comparative experiments with the state-of-the-art algorithms validate the superior performance of our method.

摘要

•A Bayesian-based multi-label online learning method for multi-label data stream classification is proposed.•Our method not only learns the label correlation from each arrived sample but also dynamically determines the number of predicted labels based on Hoeffding inequality and the label cardinality.•Our method can also handle missing values and concept drifts in the data stream effectively.•Extensive comparative experiments with the state-of-the-art algorithms validate the superior performance of our method.

论文关键词：Multi-label classification,Multi-label learning,Online learning,Data stream,Concept drift,Label correlation,Feature dependence

论文评审过程：Received 19 May 2018, Revised 12 December 2018, Accepted 4 January 2019, Available online 6 January 2019, Version of Record 18 January 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.007