A Sampling-Based Stack Framework for Imbalanced Learning in Churn Prediction

Churn prediction is gaining popularity in the research community as a powerful paradigm that supports data-driven operational decisions. Datasets related to churn prediction are often skewed with imbalanced class distribution. Data-level solutions, like over-sampling and under-sampling, have been co...

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Bibliografiske detaljer

Principais autores:	Soumi De, P. Prabu
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2022-01-01
Serier:	IEEE Access
Fag:	Churn prediction ensemble classifiers over-sampling under-sampling ensemble stack
Online adgang:	https://ieeexplore.ieee.org/document/9803037/
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