Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study
Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring the future behavior of the customers. However, the...
Main Authors: | Adnan Amin, Sajid Anwar, Awais Adnan, Muhammad Nawaz, Newton Howard, Junaid Qadir, Ahmad Hawalah, Amir Hussain |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2016-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/7707454/ |
Similar Items
-
Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique
by: Liqaa M. Shoohi, et al.
Published: (2020-04-01) -
Selective oversampling approach for strongly imbalanced data
by: Peter Gnip, et al.
Published: (2021-06-01) -
Impacto de los algoritmos de sobremuestreo en la clasificación de subtipos principales del síndrome de Guillain-Barré
by: Oscar Chávez-Bosquez, et al.
Published: (2020-12-01) -
Adaptive neighbor synthetic minority oversampling technique under 1NN outcast handling
by: Wacharasak Siriseriwan, et al.
Published: (2017-10-01) -
The Effect of the ADASYN Method on Widespread Metrics of Machine Learning Efficiency
by: Mukhit A. Baimakhanbetov, et al.
Published: (2019-07-01)