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Towards benchmarkin...
Towards benchmarking feature subset selection methods for software fault prediction
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- Afzal, Wasif (författare)
- Mälardalens högskola,Inbyggda system,Bahria University, Islamabad, Pakistan
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- Torkar, Richard, 1971 (författare)
- Blekinge Tekniska Högskola,Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),Blekinge Institute of Technology, Karlskrona, Sweden; Chalmers University of Technology, Sweden,Institutionen för programvaruteknik
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(creator_code:org_t)
- Berlin, Heidelberg : Springer, 2016
- 2016
- Engelska.
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Ingår i: Computational Intelligence and Quantitative Software Engineering. - Berlin, Heidelberg : Springer. - 9783319259642 - 9783319259628 ; , s. 33-58
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Despite the general acceptance that software engineering datasets often contain noisy, irrele- vant 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 com- ponent analysis (PCA); correlation-based feature selection (CFS); consistency-based 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 na ̈ıve 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- benchmarking
- feature subset
- selection
- fault prediction
- software
- software fault prediction
- Empirical
- Fault prediction
- Feature subset selection
Publikations- och innehållstyp
- ref (ämneskategori)
- kap (ämneskategori)
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