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A combination of multi-period training data and ensemble methods to improve churn classification of housing loan customers

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A combination of multi-period training data and ensemble methods to improve churn classification of housing loan customers

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Seppälä, T.; Thuy, L. (2018). A combination of multi-period training data and ensemble methods to improve churn classification of housing loan customers. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 141-144. doi:10.4995/CARMA2018.2018.8334

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/112049

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Title: A combination of multi-period training data and ensemble methods to improve churn classification of housing loan customers
Author:
Issued date:
Abstract:
[EN] Customer retention has been the focus of customer relationship management in the financial sector during the past decade. The first and important step in customer retention is to classify the customers into possible ...[+]
Subjects: Web data , Internet data , Big data , QCA , PLS , SEM , Conference , Churn prediction , Ensemble methods , Random forest , Gradient boosting , Multiple period training data , Housing loan churn
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
ISBN: 9788490486894
Source:
2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018).
DOI: 10.4995/CARMA2018.2018.8334
Publisher:
Editorial Universitat Politècnica de València
Publisher version: http://ocs.editorial.upv.es/index.php/CARMA/CARMA2018/paper/view/8334
Conference name: CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics
Conference place: Valencia, Spain
Conference date: Julio 12-13,2018
Type: Capítulo de libro Comunicación en congreso

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