Exploiting associations between class labels in multi-label classification

Z. Mirzamomen, Kh. Ghafooripour

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases can bring about significant improvements. In this paper, we have introduced positive, negative and hybrid relationships between the class labels for the first time and we have proposed a method to extract these relations for a multi-label classification task and consequently, to use them in order to improve the predictions made by a multi-label classifier. We have conducted extensive experiments to assess the effectiveness of the proposed method. The obtained results advocate the merits of the proposed method in improving the multi-label classification results.
Original languageEnglish
Pages (from-to)35-45
Number of pages12
JournalJournal of Artificial Intelligence and Data Mining
Volume7
Issue number1
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Multi-label classification
  • Label Relationships
  • Association rule
  • Positive relation
  • Negative relation

Cite this