Wenbin Bi1, Fang Yu2, Ning Cao3, Wei Huo3, Guangsheng Cao4, *, Xiuli Han5,
Lili Sun6, Russell Higgs7
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1837-1854, 2020, DOI:10.32604/cmc.2020.010420
Abstract Software defect feature selection has problems of feature space dimensionality
reduction and large search space. This research proposes a defect prediction feature
selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using
the two-level structure of the framework and the improved hybrid leapfrog algorithm's
own advantages, the feature values are sorted, and some features with high correlation are
selected to avoid other heuristic algorithms in the defect prediction that are easy to
produce local The case where the convergence rate of the optimal or parameter
optimization process is relatively slow. The framework improves generalization of
predictions of unknown data samples… More >
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