Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Afzal2016,
-
author = "Wasif Afzal and Richard Torkar",
-
title = "Towards Benchmarking Feature Subset Selection Methods
for Software Fault Prediction",
-
booktitle = "Computational Intelligence and Quantitative Software
Engineering",
-
publisher = "Springer",
-
year = "2016",
-
editor = "Witold Pedrycz and Giancarlo Succi and
Alberto Sillitti",
-
volume = "617",
-
series = "Studies in Computational Intelligence",
-
chapter = "3",
-
pages = "33--58",
-
keywords = "genetic algorithms, genetic programming, SBSE, Feature
subset selection, Fault prediction, Empirical",
-
isbn13 = "978-3-319-25964-2",
-
DOI = "doi:10.1007/978-3-319-25964-2_3",
-
abstract = "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); 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 naive 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.",
- }
Genetic Programming entries for
Wasif Afzal
Richard Torkar
Citations