Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection

Ahmet Tezcan Tekin*, Tolga Kaya, Ferhan Çebi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)


The fuzzy logic theorem is inherently used effectively in expressing current life problems. So, using fuzzy logic in machine learning is getting popular. In machine learning problems, especially using digital advertisement data, products/objects are being trained and predicted together, but this can cause worse prediction performance. A significant commitment of our research is, we propose a new approach for ensembling prediction with fuzzy clustering in this study. This approach aims to solve this problem. It also enables flexible clustering for the objects which have more than one cluster’s characteristics. On the other hand, our approach allows us ensembling boosting algorithms which are different types of ensembling and very popular in machine learning because of their successful performance in the literature. For testing our approach, we used an online travel agency’s digital advertisements data for predicting each hotel’s next day click amount, which is crucial for predicting marketing cost. The results show that ensembling the algorithms with a fuzzy approach has better performance result than applying algorithms individually.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques
Subtitle of host publicationSmart and Innovative Solutions - Proceedings of the INFUS 2020 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Number of pages8
ISBN (Print)9783030511555
Publication statusPublished - 2021
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey
Duration: 21 Jul 202023 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1197 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2020

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Click prediction
  • Ensemble learning
  • Fuzzy model selection
  • Fuzzy regression
  • OTA


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