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Using Feature Selection with Bagging and Rule Extraction in Drug Discovery
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
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2010 (English)Conference 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.
, Smart Innovation, Systems and Technologies, 4
Keyword [en]
feature selection, bagging, rule extraction, Machine learning
National Category
Computer Science Information Systems
URN: urn:nbn:se:hb:diva-6404Local ID: 2320/6799ISBN: 978-3-642-14615-2OAI: diva2:887092
Advances in Intelligent Decision Technologies, Second KES International Symposium IDT 2010


This work was supported by the INFUSIS project ( at the University

of Skövde, Sweden, in partnership with the Swedish Knowledge Foundation under grant


Available from: 2015-12-22 Created: 2015-12-22

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Johansson, UlfSönströd, CeciliaLöfström, Tuve
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ReferencesLink to record
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