Software fault classification using extreme learning machine: a cognitive approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)
Link
http://link.springer.com/content/pdf/10.1007/s12065-018-0193-x.pdf
Reference41 articles.
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3. Elish KO, Elish MO (2008) Predicting defect-prone software modules using support vector machines. J Syst Softw 81(5):649–660
4. Turhan B, Bener A (2009) Analysis of Naive Bayes’ assumptions on software fault data: an empirical study. Data Knowl Eng 68(2):278–290
5. Catal C, Diri B (2009) Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem. Inf Sci 179(8):1040–1058
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