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Safarkhani, F. & Moro, S. (2021). Improving the accuracy of predicting bank depositor' behavior using decision tree. Applied Sciences. 11 (19)

F. Safarkhani and S. M. Moro,  "Improving the accuracy of predicting bank depositor' behavior using decision tree", in Applied Sciences, vol. 11, no. 19, 2021

@article{safarkhani2021_1734978892463,
	author = "Safarkhani, F. and Moro, S.",
	title = "Improving the accuracy of predicting bank depositor' behavior using decision tree",
	journal = "Applied Sciences",
	year = "2021",
	volume = "11",
	number = "19",
	doi = "10.3390/app11199016",
	url = "https://www.mdpi.com/journal/applsci"
}

TY  - JOUR
TI  - Improving the accuracy of predicting bank depositor' behavior using decision tree
T2  - Applied Sciences
VL  - 11
IS  - 19
AU  - Safarkhani, F.
AU  - Moro, S.
PY  - 2021
SN  - 2076-3417
DO  - 10.3390/app11199016
UR  - https://www.mdpi.com/journal/applsci
AB  - 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%). 
ER  -