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Evaluating Ensembles on QSAR Classification
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2009 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Novel, often quite technical algorithms, for ensembling artificial neural networks are constantly suggested. Naturally, when presenting a novel algorithm, the authors, at least implicitly, claim that their algorithm, in some aspect, represents the state-of-the-art. Obviously, the most important criterion is predictive performance, normally measured using either accuracy or area under the ROC-curve (AUC). This paper presents a study where the predictive performance of two widely acknowledged ensemble techniques; GASEN and NegBagg, is compared to more straightforward alternatives like bagging. The somewhat surprising result of the experimentation using, in total, 32 publicly available data sets from the medical domain, was that both GASEN and NegBagg were clearly outperformed by several of the straightforward techniques. One particularly striking result was that not applying the GASEN technique; i.e., ensembling all available networks instead of using the subset suggested by GASEN, turned out to produce more accurate ensembles.

Place, publisher, year, edition, pages
Univeristy of Skövde , 2009.
Series
Skövde studies in Informatics, ISSN 1653-2325 ; 2009:3
Keyword [en]
classification, ensembles, QSAR, Machine learning
Keyword [sv]
data mining
National Category
Computer and Information Science Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-6294Local ID: 2320/5901OAI: oai:DiVA.org:hb-6294DiVA: diva2:886981
Conference
3rd Skövde Workshop on Information Fusion Topics
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

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Johansson, UlfLöfström, Tuve

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CiteExportLink to record
Permanent link

Direct link
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf