A deep learning approach for cost effective learning of defective prone modules
DOI:
https://doi.org/10.14419/ijet.v7i4.22672Keywords:
Software Defect Prediction, Imbalanced Data, Principle Component Analysis, Adaptive Neuro Fuzzy Inference System.Abstract
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.
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How to Cite
Received date: December 1, 2018
Accepted date: December 1, 2018
Published date: April 21, 2019