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Data science for class imbalanced and cost-sensitive data and its application to software defect prediction
Michael Siers
Charles Sturt University
Research output
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Thesis
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Doctoral Thesis
888
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Keyphrases
Data Science
100%
Cost-sensitive
100%
Software Defect Prediction
100%
Class Imbalance
100%
Knowledge Discovery
66%
Training Data
33%
Negative Effects
33%
Performance Prediction
33%
Existing Techniques
33%
Decision Forest
33%
Competitive Performance
33%
Discovered Knowledge
33%
Software Process
33%
Software Vulnerability
33%
Cost Sensitivity
33%
Aeronautics
33%
Discovery Approach
33%
Real-time Integration
33%
Computer Science
Defect Prediction
100%
Software Defect
100%
Knowledge Discovery
100%
Class Imbalance
100%
Overwhelming Majority
50%
Software Development
50%
Sensitivity Cost
50%
Negative Effect
50%
Predictive Performance
50%
Training Dataset
50%
Time Integration
50%