Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2
CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117
Abstract Software defect prediction is a research hotspot in the field of software
engineering. However, due to the limitations of current machine learning algorithms, we
can’t achieve good effect for defect prediction by only using machine learning algorithms.
In previous studies, some researchers used extreme learning machine (ELM) to conduct
defect prediction. However, the initial weights and biases of the ELM are determined
randomly, which reduces the prediction performance of ELM. Motivated by the idea of
search based software engineering, we propose a novel software defect prediction model
named KAEA based on kernel principal component analysis (KPCA), adaptive genetic
algorithm, extreme… More >
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