Linguistics

Philosophy

Biology

Computer security

Economics

Data mining

Computer science

Off-label use

Bioinformatics

Task (project management)

Pattern recognition (psychology)

Management

Multi-label classification

Feature (linguistics)

Artificial intelligence

Machine learning

Key (lock)

Knowledge-Based Systems

In multi-label learning, different labels may have their own inherent characteristics for distinguishing each other, in the meanwhile, exploiting the correlations among labels is another practical yet challenging task to improve the performance. In this work, we present a new method for the joint learning of label-specific features and label correlations. The key is the design of an optimization framework to learn the weight assignment scheme of features, and the correlations among labels are taken into account by constructing additional features at the same time. Through iteratively optimizing the two sets of unknown variables, which are referred to feature weights and label correlations-based features, label-specific features of each label are available to achieve multi-label classification. Comprehensive experiments on various multi-label data sets including two collected traditional Chinese medicine data sets reveal the advantages of our proposed algorithm.

Multi-label learning with label-specific features by resolving label correlations