Intelligent Decision Forest Models for Customer Churn Prediction - Publikacja - MOST Wiedzy

Wyszukiwarka

Intelligent Decision Forest Models for Customer Churn Prediction

Abstrakt

Customer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non‐churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones. Many solutions have been developed to address customer churn prediction (CCP), such as rule‐based and machine learning (ML) solutions. However, the issue of scalability and robustness of rule‐based customer churn solutions is a critical drawback, while the imbalanced nature of churn datasets has a detrimental impact on the prediction efficacy of conventional ML techniques in CCP. As a result, in this study, we developed intelligent decision forest (DF) models for CCP in telecommunication. Specifically, we investigated the prediction performances of the logistic model tree (LMT), random forest (RF), and Functional Trees (FT) as DF models and enhanced DF (LMT, RF, and FT) models based on weighted soft voting and weighted stacking methods. Extensive experimentation was performed to ascertain the efficacy of the suggested DF models utilizing publicly accessible benchmark telecom CCP datasets. The suggested DF models efficiently distinguish churn from non‐churn consumers in the presence of the class imbalance problem. In addition, when compared to baseline and existing ML‐based CCP methods, comparative findings showed that the proposed DF models provided superior prediction performances and optimal solutions for CCP in the telecom industry. Hence, the development and deployment of DF‐based models for CCP and applicable ML tasks are recommended.

Cytowania

  • 0

    CrossRef

  • 0

    Web of Science

  • 0

    Scopus

Autorzy (10)

Cytuj jako

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Applied Sciences-Basel nr 12,
ISSN: 2076-3417
Język:
angielski
Rok wydania:
2022
Opis bibliograficzny:
Usman-Hamzah F. E., Balogun A. O., Capretz L. F., Mojeed H., Mahamad S., Salihu S. A., Akintola A. G., Basri S., Amosa R. T., Salahdeen N. K.: Intelligent Decision Forest Models for Customer Churn Prediction// Applied Sciences-Basel -Vol. 12,iss. 16 (2022), s.8270-
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/app12168270
Weryfikacja:
Politechnika Gdańska

wyświetlono 29 razy

Publikacje, które mogą cię zainteresować

Meta Tagi