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Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine
2021
IET Software
Software defect prediction is an important software quality assurance technique. Nevertheless, the prediction performance of the constructed model is easily susceptible to irrelevant or redundant features in the software projects and is not predominant enough. To address these two issues, a novel defect prediction model called SSEPG based on Stacked Sparse Denoising AutoEncoders (SSDAE) and Extreme Learning Maching (ELM) optimised by Particle Swarm Optimisation (PSO) and another complementary
doi:10.1049/sfw2.12029
fatcat:225jhgnn6nebra7fh6fxcucqpe