Feature Weighting using Gravitational Search Algorithm in Customer Churn Prediction

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

Abstract

Churn prediction is a critical issue for telecommunications companies due to the high customer turnover. Companies implement different retention strategies in response to this challenge, including predicting customer churn. Developing a model for predicting customer churn faces difficulties due to the complex nature of the data, particularly its high dimensionality, which complicates data analysis. Techniques for reducing the number of features can be implemented to enhance accuracy and reduce processing time. However, these methods carry the risk of losing essential information. Feature weighting techniques can be utilized to avoid it, wherein the weight assigned to a feature signifies its significance in the modelling or data analysis. This study applies the Gravitational Search Algorithm to assign weights to features automatically using KNN classification. The investigation is carried out on a publicly available dataset, comparing the performance of a baseline model without feature weighting to a model that incorporates feature weighting. The findings validate that the proposed model achieves an accuracy of 89%, indicating its effectiveness in improving accuracy compared to the baseline method. This research also compares our results with others' research and shows that our model has some improvements in several metrics.

Original languageEnglish
Title of host publicationCMLDS 2024 - 2024 International Conference on Computing, Machine Learning and Data Science, Conference Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400716393
DOIs
Publication statusPublished - 12 Apr 2024
Event2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024 - Singapore, Singapore
Duration: 12 Apr 202414 Apr 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024
Country/TerritorySingapore
CitySingapore
Period12/04/2414/04/24

Keywords

  • customer churn prediction
  • feature weighting
  • gravitational search algorithm

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