Learning label-specific features and class-dependent labels for multi-label classification

J Huang, G Li, Q Huang, X Wu - IEEE transactions on …, 2016 - ieeexplore.ieee.org
IEEE transactions on knowledge and data engineering, 2016ieeexplore.ieee.org
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 …
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 stackingway, 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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