Detail publikace

Explanatory variable selection with balanced clustering in customer churn prediction

FRIDRICH, M.

Originální název

Explanatory variable selection with balanced clustering in customer churn prediction

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

FRIDRICH, M.

Vydáno

7. 7. 2019

Nakladatel

Magnanimitas

Místo

Hradec Kralove, Czech Republic

ISSN

1804-7890

Periodikum

AD Alta

Ročník

1

Číslo

9

Stát

Česká republika

Strany od

56

Strany do

66

Strany počet

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"
}