Improved Bayesian regularisation using neural networks based on feature selection for software defect prediction

Jayanthi, R.; Florence, M. Lilly

doi:10.1504/ijcat.2019.100297

Cited by 1 publication

(1 citation statement)

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“…Consequently, they have different influences on solving the software defect prediction problem of classification task 32 . And the common classification algorithms are included, K‐nearest neighbor algorithm (KNN), 33 artificial neural network (ANN), 34 Bayesian network (BN), 35,36 SVM, 13 and so on. For these methods, scholars have found that SVM has better performance in software defect prediction classifiers 8 .…”

Section: Problem Descriptionmentioning

confidence: 99%

A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

Ye¹,

Li²,

Zhang

et al. 2022

Concurrency and Computation

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Summary Data imbalance and parameter selection have always been two important factors affecting the accuracy of software defect prediction. However, the existing methods have been poor in balancing these two factors. To address this challenge, a multi‐objective software defect prediction model is employed to describe the under sampled software defect prediction problem. And in this model, defect detection rate and defect false alarm rate are deemed as two objectives which should be optimized. Simultaneously, we design a novel multi‐objective immune optimization algorithm based on the comprehensive fitness of evaluation mechanism to effectively address the employed model. In the algorithm, the original mechanism based on neighborhood individual selection is replaced based on comprehensive fitness evaluation,which have better selection ability to attaining the predicted effect of software to improving in the process of selection solution of population evolution and further effectively help decision makers choose better scheme that meets requirements. In addition, in order to verify the effectiveness of the designed algorithm, the proposed algorithm is compared on eight different public data sets. Simulation results show that the proposed algorithm has better performance in handling with the multi‐objective under sampling software defect prediction problem.

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