SMOTE Approach for Predicting the Success of Bank Telemarketing

Islam, Shifatul; Arifuzzaman, Mohammad; Islam, Saiful

doi:10.1109/times-icon47539.2019.9024630

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Ref. [37] cleaned and prepared the bank dataset [11] by considering data integrity, unified types, missing value imputation, data transformation, and data balancing, then applied five machine learning algorithms. Finally, they achieved the best accuracy by using the Gaussian NB algorithm, 88.86%.…”

Section: Discussionmentioning

confidence: 99%

Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

Safarkhani

Moro

2021

Applied Sciences

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Telemarketing is a widely adopted direct marketing technique in banks. Since customers hardly respond positively, data prediction models can help in selecting the most likely prospective customers. We aim to develop a classifier accuracy to predict which customer will subscribe to a long-term deposit proposed by a bank. Accordingly, this paper focuses on a combination of resampling, in order to reduce the imbalanced data, using feature selection, to reduce the complexity of data computing and dimension reduction of inefficiency data modeling. The performed operation has shown an improvement in the performance of the classification algorithm in terms of accuracy. The experimental results were run on a real bank dataset and the J48 decision tree achieved 94.39% accuracy prediction, with 0.975 sensitivity and 0.709 specificity, showing better results when compared to other approaches reported in the existing literature, such as logistic regression (91.79 accuracy; 0.975 sensitivity; 0.495 specificity) and Naive Bayes classifier (90.82% accuracy; 0.961 sensitivity; 0.507 specificity). Furthermore, our resampling and feature selection approach resulted in improved accuracy (94.39%) when compared to a state-of-the-art approach based on a fuzzy algorithm (92.89%).

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Section: Discussionmentioning

confidence: 99%

Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

Safarkhani

Moro

2021

Applied Sciences

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“…Oversampling techniques are frequently used to balance datasets [26]. An analogous approach has also been employed in studies conducted by [27][28][29], These three investigations employ the SMOTE oversampling technique to address class imbalances within their datasets. The outcomes of these studies uniformly assert that the utilization of SMOTE yields enhanced performance for the tested classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Element Game Classification: Effective Techniques for Handling Imbalanced Classes

2024

IJIES

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One strategy for enhancing the promotion of marketing content involves the incorporation of gamification techniques. Existing research that examines the utilization of gamification through the classification approach often overlooks the issue of class imbalance within the dataset. Class imbalance occurs when the number of instances in the minority class differs significantly from those in the dominant class. This discrepancy can cause the classification method to perform worse because most classifiers work best when the class distribution in the dataset is balanced. Multiple studies underscore the significance of addressing class imbalances as a pivotal step toward enhancing the performance of classification algorithms. This study aims to demonstrate the impact of the data imbalance challenge and to identify the superior resampling method for integration into the machine learning process. The dataset employed pertains to users of a mobile banking application in Indonesia and encompasses variables such as gamification elements, demographic information, psychological factors, and customer engagement metrics. The chosen resampling technique for this study is SMOTE-Tomek. The various classification methods utilized include linear regression, K-NN, CART, random forest, SVM, stacking ensemble, and XGBoost. Among these methods, the SMOTE-Tomek resampling approach proves most effective when combined with the stacking ensemble classification. This combination yields a remarkable accuracy rate of 98.7% during 10-fold cross-validation, along with an impressive geometric mean score of 0.99. The thorough evaluation findings demonstrated that the approach presented in this study has a promising practical applicability and effectively identifies unbalanced elementary games.

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“…A few researchers tried to improve prediction accuracy obtained by different machine learning models that was due to a data imbalance problem [8,9]. They have used Synthetic Minority Oversampling Technique (SMOTE), which is a well known approach that is capable of achieving balance in the analyzed data to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

Meshref

2020

International Journal of Circuits, Systems and Signal Processing

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The Bank Marketing data set at Kaggle is mostly used in predicting if bank clients will subscribe a long-term deposit. We believe that this data set could provide more useful information such as predicting whether a bank client could be approved for a loan. This is a critical choice that has to be made by decision makers at the bank. Building a prediction model for such high-stakes decision does not only require high model prediction accuracy, but also needs a reasonable prediction interpretation. In this research, different ensemble machine learning techniques have been deployed such as Bagging and Boosting. Our research results showed that the loan approval prediction model has an accuracy of 83.97%, which is approximately 25% better than most state-of-the-art other loan prediction models found in the literature. As well, the model interpretation efforts done in this research was able to explain a few critical cases that the bank decision makers may encounter; therefore, the high accuracy of the designed models was accompanied with a trust in prediction. We believe that the achieved model accuracy accompanied with the provided interpretation information are vitally needed for decision makers to understand how to maintain balance between security and reliability of their financial lending system, while providing fair credit opportunities to their clients.

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SMOTE Approach for Predicting the Success of Bank Telemarketing

Cited by 12 publications

References 11 publications

Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

Enhancing Element Game Classification: Effective Techniques for Handling Imbalanced Classes

Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

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