A deep learning approach for cost effective learning of defective prone modules

  • Authors

    • Satya Srinivas Maddipati KLEF,Guntur, India
    • Mallidi Srinivas KLEF,Guntur, India
    https://doi.org/10.14419/ijet.v7i4.22672

    Received date: December 1, 2018

    Accepted date: December 1, 2018

    Published date: April 21, 2019

  • 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

    Srinivas Maddipati, S., & Srinivas, M. (2019). A deep learning approach for cost effective learning of defective prone modules. International Journal of Engineering and Technology, 7(4), 5922-5925. https://doi.org/10.14419/ijet.v7i4.22672