Comparison of Adaptive Boosting and Bootstrap Aggregating Performance to Improve the Prediction of Bank Telemarketing

Agus Priyanto, Rila Mandala


Background: Telemarketing is an effective
marketing strategy lately, because it allows long-distance
interaction making it easier for marketing promotion
management to market their products. But sometimes with
incessant phone calls to clients that are less potential to cause
inconvenience, so we need predictions that produce good
probabilities so that it can be the basis for making decisions
about how many potential clients can be contacted which
results in time and costs can be minimized, telephone calls can
be more effective, client stress and intrusion will be reduced.
Method: This study will compare the classification
performance of Bank Marketing datasets from the UCI
Machine Learning Repository using data mining with the
Adaboost and Bagging ensemble approach, base algorithm
using J48 Weka, and Wrapper subset evaluation feature
selection techniques and previously data balancing was
performed on the dataset, where the expected results can be
known the best ensemble method that produces the best
performance of both.
Results: In the Bagging experiment, the best performance
of Adaboost and J48 with an accuracy rate of 86.6%, Adaboost
83.5% and J48 of 85.9%Conclusion: The conclusion obtained
from this study that the use of data balancing and feature
selection techniques can help improve classification
performance, Bagging is the best ensemble algorithm from this
study, while for Adaboost is not productive for this study
because the basic algorithm used is a strong learner where
Adaboost has Weaknesses to improve strong basic algorithm.


Telemarketing, Adaboost, Bagging, Decision Tree, J48

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Albro, W. and Linsley, C. 2001. “Credit card solicitations: Less for

more,”. Bank Marketing, 33(2): 11 [Google Scholar]

Kotsiantis, S. (2014). Bagging and boosting variants for handling

classifications problems: A survey. The Knowledge Engineering

Review, 29(1), 78-100. doi:10.1017/S0269888913000313

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and

feature selection. The Journal of Machine Learning Research, 3, pp.


Dash, M. & Liu, H. (1997). Feature selection for classification.

Intelligent Data Analysis: An International Journal, 1(3), pp. 131–

Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and

unsupervised discretization of continuous features. In Proc. Twelfth

International Conference on Machine Learning. Los Altos, CA:

Morgan Kaufmann, pp. 194–202

Han, J. ,Kamber, M., & Pei, J. (2011). Data Mining: Concepts and

Techniques, 2rd ed. The Morgan Kaufmann Series in Data

Management Systems, Jim Gray, Series Editor Morgan Kaufmann

Publishers. pp. 336–337. ISBN 1-55860-901-6

Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San

Mateo: Morgan Kaufmann.

Yoav Freund, Robert Schapire. (1999). A Short Introduction to

Boosting, Journal of Japanese Society for Artificial Intelligence,

(5):771-780, September, 1999,

Gu, Q., Cai, Z, Zhu, L., & Huang, B. (2008). Data Mining on

Imbalanced Data Sets. Proceedings of International Conference on

Advanced Computer Theory and Engineering, Phuket, pp. 1020–



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