A deep learning approach for cost effective learning of defective prone modules - Srinivas Maddipati, Satya; Srinivas, Mallidi (International Journal of Engineering and Technology, 2019)
Software Defect Prediction is a cost effective problem, in which the cost of majority class (Non defective) is low compared with the cost of minority class ( Defective). Learning from imbalanced data bias the classifier towards majority class. In this paper we are proposing a deep learning approach for classifying Imbalanced and Cost effective data. We applied Principle Component Analysis for feature selection and then constructed a classifier using Adaptive Neuro Fuzzy Inference System. The performance of the classifier was evaluated using AuC measures. We observed the performance of the classifier was improved compared with neural networks.
Keywords: Software Defect Prediction; Imbalanced Data; Principle Component Analysis; Adaptive Neuro Fuzzy Inference System.