Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets

Abstract

Many science, technology and innovation (STI) resources are attached with several different labels. To assign automatically the resulting labels to an interested instance, many approaches with good performance on the benchmark datasets have been proposed for multilabel classification task in the literature. Furthermore, several open-source tools implementing these approaches have also been developed. However, the characteristics of real-world multilabel patent and publication datasets are not completely in line with those of benchmark ones. Therefore, the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.

Keywords

• multi-label classification
• real-world datasets
• hierarchical structure
• classification system
• label correlation
• machine learning