Feature Selection in Hierarchical Feature Spaces

Ristoski, Petar ; Paulheim, Heiko

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DOI: https://doi.org/10.1007/978-3-319-11812-3_25
URL: https://madoc.bib.uni-mannheim.de/37063
Additional URL: http://www.slideshare.net/pristoski/ds2014-feature...
URN: urn:nbn:de:bsz:180-madoc-370638
Document Type: Conference or workshop publication
Year of publication: 2014
Book title: Discovery Science : 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 8777
Page range: 288-300
Conference title: DS 2014
Location of the conference venue: Bled, Slovenia
Date of the conference: 08 Oct-10 Oct 2014
Publisher: Džeroski, Sašo
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-319-11811-6 , 978-3-319-11812-3
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
School of Business Informatics and Mathematics > Information Systems V: Web-based Systems (Bizer 2012-)
Subject: 004 Computer science, internet
Abstract: Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features' relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies.

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