Using Feature Selection with Bagging and Rule Extraction in Drug DiscoveryShow others and affiliations
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]
This paper investigates different ways of combining feature selection with
bagging and rule extraction in predictive modeling. Experiments on a large number of
data sets from the medicinal chemistry domain, using standard algorithms implemented
in theWeka data mining workbench, show that feature selection can lead to significantly
improved predictive performance.When combining feature selection with bagging, employing
the feature selection on each bootstrap obtains the best result.When using decision
trees for rule extraction, the effect of feature selection can actually be detrimental,
unless the transductive approach oracle coaching is also used. However, employing oracle
coaching will lead to significantly improved performance, and the best results are
obtainedwhen performing feature selection before training the opaque model. The overall
conclusion is that it can make a substantial difference for the predictive performance
exactly how feature selection is used in conjunction with other techniques.
Place, publisher, year, edition, pages
Springer-Verlag Berlin Heidelberg , 2010.
Series
Smart Innovation, Systems and Technologies ; 4
Keywords [en]
feature selection, bagging, rule extraction, Machine learning
National Category
Computer Sciences Information Systems
Identifiers
URN: urn:nbn:se:hb:diva-6404Local ID: 2320/6799ISBN: 978-3-642-14615-2 (print)OAI: oai:DiVA.org:hb-6404DiVA, id: diva2:887092
Conference
Advances in Intelligent Decision Technologies, Second KES International Symposium IDT 2010
Note
Sponsorship:
This work was supported by the INFUSIS project (www.his.se/infusis) at the University
of Skövde, Sweden, in partnership with the Swedish Knowledge Foundation under grant
2008/0502.
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