Abstract
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.
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Kurach, K., Pawłowski, K., Romaszko, Ł., Tatjewski, M., Janusz, A., Nguyen, H.S. (2013). Multi-label Classification of Biomedical Articles. In: Bembenik, R., Skonieczny, L., Rybinski, H., Kryszkiewicz, M., Niezgodka, M. (eds) Intelligent Tools for Building a Scientific Information Platform. Studies in Computational Intelligence, vol 467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35647-6_15
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DOI: https://doi.org/10.1007/978-3-642-35647-6_15
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