Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers

Hyunha NAM  Hirotaka HACHIYA  Masashi SUGIYAMA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.8   pp.1871-1874
Publication Date: 2013/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.1871
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Fundamentals of Information Systems
Keyword: 
multi-label classification,  least-squares probabilistic classifier,  

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Summary: 
Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.