Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction Using Filter-Based Feature Selection Methods
No Thumbnail Available
Date
2018-03
Authors
Akintola, Abimbola G.
Balogun, Abdullateef O.
Lafenwa-Balogun, Fatimah
Mojeed, Hameed A.
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Engineering, Federal University Oye-Ekiti, Ekiti State, Nigeria.
Abstract
Classification techniques is a popular approach to predict software defects and it involves categorizing modules, which is
represented by a set of metrics or code attributes into fault prone (FP) and non-fault prone (NFP) by means of a classification model.
Nevertheless, there is existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing
knowledge and useful pattern. Therefore, researchers need to retrieve relevant data from huge records using feature selection methods.
Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this
study, the researchers investigated the effect of filter feature selection on classification techniques in software defects prediction. Ten
publicly available datasets of NASA and Metric Data Program software repository were used. The topmost discriminatory attributes of the
dataset were evaluated using Principal Component Analysis (PCA), CFS and FilterSubsetEval. The datasets were classified by the selected
classifiers which were carefully selected based on heterogeneity. Naïve Bayes was selected from Bayes category Classifier, KNN was
selected from Instance Based Learner category, J48 Decision Tree from Trees Function classifier and Multilayer perceptron was selected
from the neural network classifiers. The experimental results revealed that the application of feature selection to datasets before
classification in software defects prediction is better and should be encouraged and Multilayer perceptron with FilterSubsetEval had the best
accuracy. It can be concluded that feature selection methods are capable of improving the performance of learning algorithms in software
defects prediction.
Description
Keywords
Data Mining, Machine learning, Software Defeats
Citation
Akintola, A. G., Balogun, A. O., Lafenwa-Balogun, F., & Mojeed, H. A (2018): Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction using Filter-Based Feature Selection Method. FUOYE Journal of Engineering and Technology. 3(1). 133 - 137