Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification

Jun Huang

doi:10.1109/TKDE.2016.2608339

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Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification

2016-12-01

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DOI：10.1109/TKDE.2016.2608339

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Abstract

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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.

Keywords:

Multi-label classification

label correlation

feature selection

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