Logistic Regression Learning Model for Handling Concept Drift with Unbalanced Data in Credit Card Fraud Detection System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)


Credit card is the well-accepted manner of remission in financial field. With the rising number of users across the globe, risks on usage of credit card have also been increased, where there is danger of stealing credit card details and committing frauds. Traditionally, machine learning area has been developing algorithms that have certain assumptions on underlying distribution of data, such as data should have predetermined and fixed distribution. Real-word situations are different than this constrained model; rather applications often face problems such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also frequent that results in the sudden drifts in the concepts. These issues have been separately addressed by the researchers. This paper aims to propose a universal framework using logistic regression model that intelligently tackles issues in the incremental learning for the assessment of credit risks.


Logistic regression learning Concept drift Class imbalance Credit card fraud detection 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Dr. D.Y. Patil School of Engineering and Technology, Savitribai Phule Pune UniversityPuneIndia

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