Abstract
Software Defect Prediction is based on datasets that are imbalanced and therefore limit the use of machine learning based classification. Ensembles of genetic classifiers indicate good performance and provide a promising solution to this problem. To further examine this solution, we performed additional experiments in that direction. In this paper we report preliminary results obtained by using a Matlab variant of NSGA-II in combination with four simple voting strategies on three subsequent releases of the Eclipse Plug-in Development Environment (PDE) project. Preliminary results indicate that the voting procedure might influence software defect prediction performances.
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Acknowledgments
The work presented in this paper is supported by the University of Rijeka Research Grant 13.09.2.2.16.
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Rubinić, E., Mauša, G., Grbac, T.G. (2015). Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies. In: Barros, M., Labiche, Y. (eds) Search-Based Software Engineering. SSBSE 2015. Lecture Notes in Computer Science(), vol 9275. Springer, Cham. https://doi.org/10.1007/978-3-319-22183-0_33
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DOI: https://doi.org/10.1007/978-3-319-22183-0_33
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