Enhancing Telecommunication Churn Prediction using Adapted Simulated
Annealing-based Classification.
- A. Omar
, - M. Mgala,
- F. Mwakondo
Abstract
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In the fast-growing and competitive telecommunications business,
customer churn is a major problem since it directly affects the
company’s revenue. Therefore, it is crucial to identify the factors that
influence customer churn. Traditional Feature selection has yielded
fewer effective results. Few studies have used simulated annealing for
feature selection and classification for churn prediction in Telkom.
Most of these studies used fixed cooling schedules after each iteration
of the process. Very Slow cooling demands a substantial amount of
computation time and, therefore can be impractical for large or complex
problems where faster convergence is desired. Fast cooling can lead to
premature convergence and getting trapped in local minima due to faster
cooling rates. This results in the system settling into a local minimum,
which is a solution that is better than its immediate neighbors but not
the best overall. To strike a balance between efficiency and
computational time, this study proposes developing an adapted simulated
annealing approach. This method dynamically adjusts the cooling rate
based on feedback on the progress of solution quality of the search
process, ensuring adequate exploration while progressively focusing on
convergence. In this study, we use accuracy as the solution quality of
algorithms. We conducted experiments using machine learning algorithms
like Random Forest, Decision Trees, XGBoost and Adaboost blended with
Adapted simulated annealing (ASA) using publicly available
telecommunications datasets. Results show our ASA method outperforms
traditional SA cooling schedules and other metaheuristic algorithms in
accuracy, precision, ROC, recall, and F1-score.Submission Checks Completed Assigned to Editor
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