Multi-label methods for prediction with sequential data
作者：
Highlights：
• Drawing connections between learning for multi-label and sequential data.
• A unified view between multi-label and sequential classifiers.
• A novel Markov model-inspired method for multi-label (and sequence) classification.
• A novel multi-label-inspired method for sequence (and multi-label) classification.
• An empirical comparison with related methods, on real-world datasets, demonstrating the competitiveness of proposed methods.
摘要
Highlights•Drawing connections between learning for multi-label and sequential data.•A unified view between multi-label and sequential classifiers.•A novel Markov model-inspired method for multi-label (and sequence) classification.•A novel multi-label-inspired method for sequence (and multi-label) classification.•An empirical comparison with related methods, on real-world datasets, demonstrating the competitiveness of proposed methods.
论文关键词：Multi-label classification,Problem transformation,Sequential data,Sequence prediction,Markov models
论文评审过程：Received 16 October 2015, Revised 23 August 2016, Accepted 19 September 2016, Available online 21 September 2016, Version of Record 28 September 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.015
