Class Imbalance Learning of Defective Prone Modules Using Adaptive Neuro Fuzzy Inference System

Tracking #: 514-1494

Responsible editor: 

Evangelos Pournaras

Submission Type: 

Research Paper


Defect Identification is a major challenge in Software Development Process. Identifying a Defect in early stages reduces the cost of Software Development rather than the later stages. This motivates Demand for applying Data mining techniques for Predicting Software Defects. But the datasets available for predicting software defects are imbalance in nature. Due to imbalance nature of data available, the classifier performance will be degraded even though the classifier has low error rate. To improve the performance of classifier, In this paper, we applied Cost Sensitive Adaptive Neuro Fuzzy Inference System(CSANFIS). The performance of the classifier is measured using AuC(Area under ROC curves) values. We observed AuC value for CSANFIS was high compared to existing different over sampling & under sampling methods.



  • Reviewed

Data repository URLs:

Date of Submission: 

Thursday, August 3, 2017

Date of Decision: 

Thursday, September 14, 2017

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