An optimization approach to identify the relationship between features and output of a multi-label classifier

Mammadov, Musa, Rubinov, Alex and Yearwood, John Leighton 2007, An optimization approach to identify the relationship between features and output of a multi-label classifier. In Pardalos, Panos M., Boginski, Vladimir L. and Vazacopoulos, Alkis (ed), Data Mining in Biomedicine, Springer, Boston, Mass., pp.141-167, doi: 10.1007/978-0-387-69319-4_9.


Title An optimization approach to identify the relationship between features and output of a multi-label classifier
Author(s) Mammadov, MusaORCID iD for Mammadov, Musa orcid.org/0000-0002-2600-3379
Rubinov, Alex
Yearwood, John LeightonORCID iD for Yearwood, John Leighton orcid.org/0000-0002-7562-6767
Title of book Data Mining in Biomedicine
Editor(s) Pardalos, Panos M.
Boginski, Vladimir L.
Vazacopoulos, Alkis
Publication date 2007
Series Springer Optimization and Its Applications Book Series (SOIA)
Chapter number 9
Total chapters 28
Start page 141
End page 167
Total pages 27
Publisher Springer
Place of Publication Boston, Mass.
Keyword(s) knowledge representation
adverse drug reaction
text categorization
multi-label classification
suspected drugs
Summary Multi-label classification is an important and difficult problem that frequently arises in text categorization. The accurate identification of drugs which are responsible for reactions that have occurred is one of the important problems of adverse drug reactions (ADR). In this chapter we consider the similarities of these two problems and analyze the usefulness of drug reaction relationships for the prediction of possible reactions that may occur. We also introduce a new method for the determination of responsibility for subsets of drug(s), out of all drugs taken by a particular patient, in reactions that have been observed. This method is applied for the evaluation of the level of correctness of suspected drugs reported in Cardiovascular type reactions in the ADRAC database. The problem of interaction of drugs is also considered.
ISBN 9780387693187
ISSN 1931-6828
1931-6836
Language eng
DOI 10.1007/978-0-387-69319-4_9
Indigenous content off
HERDC Research category B1.1 Book chapter
Copyright notice ©2007, Springer Science+Business Media
Persistent URL http://hdl.handle.net/10536/DRO/DU:30126033

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