For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers
Hyunha NAM Hirotaka HACHIYA Masashi SUGIYAMA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2013/08/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Fundamentals of Information Systems
multi-label classification, least-squares probabilistic classifier,
Full Text: PDF(185.7KB)>>
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.