Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients

NH Niloy; MAI Navid

doi:doi:10.11648/j.ajdmkd.20180301.11

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Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients

NH Niloy, MAI Navid

Published in American Journal of Data Mining and Knowledge Discovery (Volume 3, Issue 1)

Received: 17 October 2017 Accepted: 1 November 2017 Published: 10 January 2018

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Abstract

Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naïve Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 3, Issue 1)
DOI	10.11648/j.ajdmkd.20180301.11
Page(s)	1-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Machine Learning, Naïve Bayesian Classifier, Decision Trees, Predictive Model

References

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[2]	Koh, H. C., & Chan, K. L. G. (2002). Data mining and customer relationship marketing in the banking industry. Singapore Management Review, 24(2), 1–27.
[3]	Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 312–329.
[4]	Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635.
[5]	Desai, V. S., Crook, J. N., & Overstreet, G. A. A. (1996). Comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24–37.
[6]	Berry, M., & Linoff, G. (2000). Mastering data mining: The art and science of customer relationship management. New York: John Wiley & Sons, Inc. Chou, M. (2006). Cash and credit card crisis in Taiwan. Business Weekly, 24–27.
[7]	Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. San Fransisco: Morgan Kaufmann.
[8]	Hand, D. J., Mannila, H., & Smyth, P. (2001). Data mining: Practical machine learning tools and techniques. Cambridge: MIT Press.
[9]	Paolo, G. (2001). Bayesian data mining, with application to bench marking and credit scoring. Applied Stochastic Models in Business and Society, 17, 69–81.
[10]	Witten, I. H., & Frank, E. (1999). Data mining: Practical machine learning tools and techniques with java implementations. San Fransisco: Morgan Kaufman.
[11]	Lee, T. S., Chiu, C. C., Lu, C. J., & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23(3), 245–254.

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APA Style

NH Niloy, MAI Navid. (2018). Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. American Journal of Data Mining and Knowledge Discovery, 3(1), 1-12. https://doi.org/10.11648/j.ajdmkd.20180301.11

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ACS Style

NH Niloy; MAI Navid. Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. Am. J. Data Min. Knowl. Discov. 2018, 3(1), 1-12. doi: 10.11648/j.ajdmkd.20180301.11

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AMA Style

NH Niloy, MAI Navid. Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. Am J Data Min Knowl Discov. 2018;3(1):1-12. doi: 10.11648/j.ajdmkd.20180301.11

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@article{10.11648/j.ajdmkd.20180301.11,
  author = {NH Niloy and MAI Navid},
  title = {Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {3},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.ajdmkd.20180301.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20180301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20180301.11},
  abstract = {Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naïve Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.},
 year = {2018}
}

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TY  - JOUR
T1  - Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients
AU  - NH Niloy
AU  - MAI Navid
Y1  - 2018/01/10
PY  - 2018
N1  - https://doi.org/10.11648/j.ajdmkd.20180301.11
DO  - 10.11648/j.ajdmkd.20180301.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20180301.11
AB  - Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naïve Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.
VL  - 3
IS  - 1
ER  -

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Author Information

NH Niloy

Department of Science, Ruhea College, Rangpur, Bangladesh
MAI Navid

Department of Science, Ruhea College, Rangpur, Bangladesh

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Plain Text BibTeX RIS

APA Style

NH Niloy, MAI Navid. (2018). Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. American Journal of Data Mining and Knowledge Discovery, 3(1), 1-12. https://doi.org/10.11648/j.ajdmkd.20180301.11

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ACS Style

NH Niloy; MAI Navid. Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. Am. J. Data Min. Knowl. Discov. 2018, 3(1), 1-12. doi: 10.11648/j.ajdmkd.20180301.11

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AMA Style

NH Niloy, MAI Navid. Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. Am J Data Min Knowl Discov. 2018;3(1):1-12. doi: 10.11648/j.ajdmkd.20180301.11

Copy | Download

@article{10.11648/j.ajdmkd.20180301.11,
  author = {NH Niloy and MAI Navid},
  title = {Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {3},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.ajdmkd.20180301.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20180301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20180301.11},
  abstract = {Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naïve Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients
AU  - NH Niloy
AU  - MAI Navid
Y1  - 2018/01/10
PY  - 2018
N1  - https://doi.org/10.11648/j.ajdmkd.20180301.11
DO  - 10.11648/j.ajdmkd.20180301.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20180301.11
AB  - Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naïve Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.
VL  - 3
IS  - 1
ER  -

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