Multi-label classification of biomedical articles

Karol Kurach, Krzysztof Pawłowski, Łukasz Romaszko, Marcin Tatjewski, Andrzej Janusz, Hung Son Nguyen

Research output: Chapter in Book/Report/Conference proceedingChapter


In this paper we investigate a special case of classification problem, called multi-label learning, where each instance (or object) is associated with a set of target labels (or simple decisions). Multi-label classification is one of the most important issues in semantic indexing and text categorization systems. Most of multi-label classification methods are based on combination of binary classifiers, which are trained separately for each label. In this paper we concentrate on the application of ensemble technique to multi-label classification problem. We present the most recent ensemble methods for both the binary classifier training phase as well as the combination learning phase. The proposed methods have been implemented within the SONCA system which is a part of SYNAT project. We present some experiment results performed on PubMed Central biomedical articles database.

Original languageEnglish (US)
Title of host publicationIntelligent Tools for Building a Scientific Information Platform
Subtitle of host publicationAdvanced Architectures and Solutions
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783642356469
StatePublished - 2013
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • Data Mining
  • Explicit Semantic Analysis
  • MeSH
  • Multi-label Classification
  • PubMed
  • Topical Classification

ASJC Scopus subject areas

  • Artificial Intelligence


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