(1) * Zahra Putri Agusta Mail (Surya University, Indonesia)
(2) Adiwijaya Adiwijaya Mail (Telkom University, Indonesia)
*corresponding author

Abstract


Customer churn prediction is one of the company’s effort to anticipate the loss of revenue. Some data mining classification techniques can be used to predict customer churn. However, these techniques could become less optimal when faced with imbalanced data conditions, and customer churn data has imbalanced data characteristic. There are two approaches that can solve these problems, namely sampling method and algorithm approach. This paper used the algorithm approach, which combine the process balanced data and algorithm cohesively, because the consistency of original data distribution will be kept the same as the training data. This will provide more valid data and prediction results that can better represent real conditions. In line with this, we proposed a Modified Balanced Random Forest (MBRF) algorithm as a classification technique to address imbalanced data. The MBRF process changes the process in a Balanced Random Forest by applying an under-sampling strategy based on clustering techniques for each data bootstrap decision tree in the Random Forest algorithm. To find the optimal performance of our proposed method compared with four clustering techniques, like: K-MEANS, Spectral Clustering, Agglomerative Clustering, and Ward Hierarchical Clustering. The experimental result show the Ward Hierarchical Clustering Technique achieved optimal performance, also the proposed MBRF method yielded better performance compared to the Balanced Random Forest (BRF) and Random Forest (RF) algorithms, with a sensitivity value or true positive rate (TPR) of 93.42%, a specificity or true negative rate (TNR) of 93.60%, and the best AUC accuracy value of 93.51%. Moreover, MBRF also reduced process running time.

Keywords


Imbalanced data; Random forest algorithm; Balanced random forest ; Customer churn; Classification technique; Machine learning

          

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International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan , and UTM Big Data Centre - Universiti Teknologi Malaysia
Published by Universitas Ahmad Dahlan
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