Wrapper Feature Selection based Heterogeneous Classifiers for Software Defect Prediction
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Date
2019-02
Journal Title
Journal ISSN
Volume Title
Publisher
Adeleke University, Ede.
Abstract
The performance of Software Defect Prediction (SDP) models depends on the
quality of dataset used for training the models. The high dimensionality of
software metric features has been noted as a data quality problem which
affects the performance of SDP models. This makes it crucial to apply feature
selection (FS) to SDP since FS can remove irrelevant and redundant software
metric features. In this study, the effect of wrapper-based FS methods on
classification techniques in SDP was investigated. The wrapper FS methods
were based on different search methods; Best First Search (BFS), Genetic
Search (GS), Greedy Stepwise Search (GSS) and Multi-Objective
Evolutionary Search (MOES) so as to investigate their respective effect on
classifiers in SDP. Five (5) publicly available software defect datasets were
used. These datasets were classified by the individual classifiers which were
carefully selected based on their characteristics hence the heterogeneity.
Naïve Bayes (NB) was selected from Bayes category Classifier, K-Nearest
Neighbor (KNN) was selected from Instance-Based Learner category and
(J48) Decision Tree from Trees Function classifier. The experimental results
clearly showed that the application of wrapper FS method to datasets before
classification in SDP is better and should be encouraged as NB with GS based
Wrapper Method had the best accuracy performance. It can be concluded that
FS methods are capable of improving the performance of predictive models in
SDP.
Description
Keywords
Software Defect Prediction, Feature Selection, Classification, Data Quality Problem
Citation
Mabayoje, M.A., Balogun, A.O., Bello, S.M, Atoyebi, J.O., Mojeed, H.A and Ekundayo, A.H. (2019). Wrapper Feature Selection based Heterogeneous Classifiers for Software Defect Prediction. Adeleke University Journal of Engineering and Technology (AUJET). 2(1), 1-11.