Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
Towards benchmarking feature subset selection methods for software fault prediction
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2016 (English)In: Studies in Computational Intelligence, Springer, 2016, 617, Vol. 617, 33-58 p.Chapter in book (Refereed)
Resource type
Text
Abstract [en]

Despite the general acceptance that software engineering datasets often contain noisy, irrelevant or redundant variables, very few benchmark studies of feature subset selection (FSS) methods on real-life data from software projects have been conducted. This paper provides an empirical comparison of state-of-the-art FSS methods: information gain attribute ranking (IG); Relief (RLF); principal component analysis (PCA); correlation-based feature selection (CFS); consistencybased subset evaluation (CNS); wrapper subset evaluation (WRP); and an evolutionary computation method, genetic programming (GP), on five fault prediction datasets from the PROMISE data repository. For all the datasets, the area under the receiver operating characteristic curve—the AUC value averaged over 10-fold cross-validation runs—was calculated for each FSS method-dataset combination before and after FSS. Two diverse learning algorithms, C4.5 and naïve Bayes (NB) are used to test the attribute sets given by each FSS method. The results show that although there are no statistically significant differences between the AUC values for the different FSS methods for both C4.5 and NB, a smaller set of FSS methods (IG, RLF, GP) consistently select fewer attributes without degrading classification accuracy. We conclude that in general, FSS is beneficial as it helps improve classification accuracy of NB and C4.5. There is no single best FSS method for all datasets but IG, RLF and GP consistently select fewer attributes without degrading classification accuracy within statistically significant boundaries. © Springer International Publishing Switzerland 2016.

Place, publisher, year, edition, pages
Springer, 2016, 617. Vol. 617, 33-58 p.
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 617
Keyword [en]
Empirical; Fault prediction; Feature subset selection
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-11573DOI: 10.1007/978-3-319-25964-2_3ScopusID: 2-s2.0-84955278082OAI: oai:DiVA.org:bth-11573DiVA: diva2:900079
Available from: 2016-02-03 Created: 2016-02-03 Last updated: 2016-09-01Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Torkar, Richard
By organisation
Department of Software Engineering
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 228 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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