Publication detail

Explanatory variable selection with balanced clustering in customer churn prediction

FRIDRICH, M.

Original Title

Explanatory variable selection with balanced clustering in customer churn prediction

Type

journal article in Web of Science

Language

English

Original Abstract

The interest in customer relationship management has been fueled by the broad adoption of customer-centric paradigm, rapid growth in data collection, and technology advances for more than the past 15 years. It becomes hard to identify and interpret meaningful patterns in customer behavior; thus the goal of the paper is to compare multiple explanatory variable selection procedures and their effect on a customer churn prediction model. Filter and wrapper concepts of variable selection are examined, moreover, the runtime of the machine learning pipeline is improved by the novel idea of balanced clustering. Classification learners are incorporated with regard to simplicity and interpretability (LOGIT, CIT) and complexity and proven performance on a given dataset (RF, RBF-SVM). In addition, we show that when combined with learner capable of embedded feature selection, explicit variable selection scheme does not necessarily lead to performance improvement. On the other hand, RBF-SVM learner with no such ability benefits from relevant selection procedure in all expected aspects, including classification performance and runtime, problem comprehensibility, data storage.

Keywords

customer churn prediction; customer relationship management; feature selection, machine learning; variable importance

Authors

FRIDRICH, M.

Released

7. 7. 2019

Publisher

Magnanimitas

Location

Hradec Kralove, Czech Republic

ISBN

1804-7890

Periodical

AD Alta

Year of study

1

Number

9

State

Czech Republic

Pages from

56

Pages to

66

Pages count

11

URL

http://www.magnanimitas.cz/ADALTA/0901/papers/A_fridrich.pdf

BibTex

@article{BUT157810,
  author="Martin {Fridrich}",
  title="Explanatory variable selection with balanced clustering in customer churn prediction",
  journal="AD Alta",
  year="2019",
  volume="1",
  number="9",
  pages="56--66",
  issn="1804-7890",
  url="http://www.magnanimitas.cz/ADALTA/0901/papers/A_fridrich.pdf"
}