Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 28, Issue 12, Pages 3309-3323Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2608339
Keywords
Multi-label classification; label correlation; feature selection
Categories
Funding
- National Basic Research Program of China (973 Program) [2012CB316400, 2015CB351802]
- Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China [IRT13059]
- National Natural Science Foundation of China [61303153, 61332016, 61229301, 61620106009]
- Bureau of Frontier Science and Education of Chinese Academy of Sciences [QYZDJ-SSW-SYS013]
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Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework, and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent Labels in a sparse stacking way, denoted as LLSF-DL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods.
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