Multi-label methods for prediction with sequential data

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• 作者：Jesse Read^a ; ^b ; ^{jesse.read@polytechnique.edu}Author Vitae ; Luca Martino^c ; ^dAuthor Vitae ; Jaakko Hollmé ; n^bAuthor Vitae
• 关键词：Multi-label classification ; Problem transformation ; Sequential data ; Sequence prediction ; Markov models
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：45-55
• 全文大小：1274 K
• 卷排序：63

文摘

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.