Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem

Michael J. Siers; Md Zahidul Islam

doi:10.1016/j.is.2015.02.006

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Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem

Michael J. Siers Md Zahidul Islam

Year: 2015 Journal: Information Systems Vol: 51 Pages: 62-71 Publisher: Elsevier BV

DOI: 10.1016/j.is.2015.02.006

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Keywords:

Computer science Decision tree Machine learning Classifier (UML) Voting Artificial intelligence Random forest Software Majority rule Data mining Software bug Class (philosophy) Process (computing)

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26.08

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0.99

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Topics

Software Engineering Research

Physical Sciences → Computer Science → Information Systems

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Software Reliability and Analysis Research

Physical Sciences → Computer Science → Software

Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem

Metrics

Citation History

Topics

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