Random Search-Based Parameter Optimization on Binary Classifiers for Software Defect Prediction
DOI:
https://doi.org/10.26555/jiteki.v10i2.28973Keywords:
Software Defect Prediction, Software Metrics, Machine Learning, Classification, Decision Tree, Support Vector Machine, Naïve BayesAbstract
Machine learning classifiers consist of a set of parameters. The efficiency of these classifiers in the context of software defect prediction is greatly impacted by the parameters chosen to execute the classifiers. These parameters can be optimized to achieve more accurate results. In this research, the efficiency of binary classifiers for software defect prediction is analyzed through parameter optimization using random search technique. Three heterogeneous binary classifiers i.e., Decision tree, Support vector machine, and Naïve Bayes are selected to examine the results of parameter optimization. The experiments were performed on seven publicly available NASA Datasets. The dataset was split into 70-30 proportions with class preservation. To evaluate the performance; five statistical measures have been implemented i.e., precision, recall, F-Measure, the area under the curve (AUC), and accuracy. The findings of the research revealed that there is significant improvement in accuracy for each classifier. On average, decision tree improved from 88.1% to 95.4%; support vector machine enhanced the accuracy from 94.3% to 99.9%. While Naïve Bayes showed an accuracy boost from 74.9% to 85.3%. This research contributes to the field of machine learning by presenting comparative analysis of accuracy improvements using default parameters and optimized parameters through random search. The results presented that he performance of binary classifiers in the context of software prediction can be enhanced to a great extent by employing parameter optimization using random search.
Downloads
Published
Issue
Section
License
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License