Uma R. Salunkhe

Work place: Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, 411041, India



Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Networks, Data Structures and Algorithms, Information-Theoretic Security


Uma R. Salunkhe has completed her Master of Engineering in CSE-IT from the University of Pune. She is research scholar at Smt. Kashibai Navale College of Engineering, Pune and working as an Assistant Professor at Sinhgad College of Engineering, Pune, Maharashtra, India. She has 15 years of experience in teaching field. She has published 10 articles in various international journals and conferences. Her area of interest includes Security in Networks and Machine Learning.

Author Articles
A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

By Uma R. Salunkhe Suresh N. Mali

DOI:, Pub. Date: 8 May 2018

Customer retention is becoming a key success factor for many business applications due to increasing market competition. Especially telecom companies are facing this challenge with a rapidly increasing number of service providers. Hence there is need to focus on customer churn prediction in order to detect the customers that are likely to churn i.e. switch from one service provider to another. Several data mining techniques are applied for classifying customers into the churn and non-churn category. But churn prediction applications comprise an imbalanced distribution of the dataset.
One of the commonly used techniques to handle imbalanced data is re-sampling of data as it is independent of the classifier being used. In this paper, we develop a hybrid re-sampling approach named SOS-BUS by combining well known oversampling technique SMOTE with our novel under-sampling technique. Our methodology aims to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random under-sampling. Experimental results show that the proposed approach outperforms the other reference techniques in terms of Area under ROC Curve (AUC).

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