Entropy Based Trees to Support Decision Making for Customer Churn Management

• Authors: K. Gajowniczek , A. Orłowski , T. Ząbkowski
• Languages of publication: EN

Abstracts

EN

In this work we analyze empirically customer churn problem from a physical point of view to provide objective, data driven and significant answers to support decision making process in business application. In particular, we explore different entropy measures applied to decision trees and assess their performance from the business perspective using set of model quality measures often used in business practice. Additionally, the decision trees are compared with logistic regression and two machine learning methods - neural networks and support vector machines.

Keywords

EN

05.45.Tp; 05.40.Ca; 07.05.Kf; 07.05.Mh

Discipline

• 05.40.Ca: Noise
• 05.45.Tp: Time series analysis
• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence
• 07.05.Kf: Data analysis: algorithms and implementation; data management(for data analysis in nuclear physics, see 29.85.-c)

• Journal: Acta Physica Polonica A
• Year: 2016
• Volume: 129
• Issue: 5
• Pages: 971-979

Dates

published

2016-05

Contributors

author

K. Gajowniczek

• Department of Informatics, Faculty of Applied Informatics and Mathematics, WULS-SGGW, Nowoursynowska 159, 02-776 Warsaw, Poland

author

A. Orłowski

• Department of Informatics, Faculty of Applied Informatics and Mathematics, WULS-SGGW, Nowoursynowska 159, 02-776 Warsaw, Poland

author

T. Ząbkowski

• Department of Informatics, Faculty of Applied Informatics and Mathematics, WULS-SGGW, Nowoursynowska 159, 02-776 Warsaw, Poland

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Identifiers

DOI

10.12693/APhysPolA.129.971