Novel algorithms for cost-sensitive classification and knowledge discovery in class imbalanced datasets with an application to NASA software defects

Keyphrases

• Knowledge Discovery 100%
• Novel Algorithm 100%
• Software Defect Prediction 100%
• Cost-sensitive Classification 100%
• Software Defect 100%
• Class-imbalanced Datasets 100%
• Aeronautics 100%
• Decision Forest 66%
• Cost-sensitive 66%
• Class Imbalance 66%
• Knowledge Discovery Process 66%
• Decision Tree 33%
• Individual Choice 33%
• Machine Learning 33%
• Learning Algorithm 33%
• Interesting Patterns 33%
• Software Project 33%
• Traditional Learning 33%
• Source Code 33%
• Random Forest Classifier 33%
• Interestingness 33%
• Manual Inspection 33%
• Monetary Cost 33%
• Cost-sensitive Learning 33%
• Extracting Knowledge 33%
• Sensitive Knowledge 33%
• Financial Gain 33%

Computer Science

• Software Defect 100%
• Knowledge Discovery 100%
• Defect Prediction 75%
• Discovery Process 50%
• Class Imbalance 50%
• Individual Decision 25%
• Source Codes 25%
• Software Project 25%
• Financial Gain 25%
• Extracting Knowledge 25%
• Decision Trees 25%
• Learning Algorithm 25%
• Machine Learning 25%
• Learning System 25%