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An Approach to Detect Credit Card Frauds using Attribute Selection and Ensemble Techniques

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Shivangi Sharma, Puneet Mittal, Geetika
10.5120/ijca2018916482

Shivangi Sharma, Puneet Mittal and Geetika. An Approach to Detect Credit Card Frauds using Attribute Selection and Ensemble Techniques. International Journal of Computer Applications 180(21):1-6, February 2018. BibTeX

@article{10.5120/ijca2018916482,
	author = {Shivangi Sharma and Puneet Mittal and Geetika},
	title = {An Approach to Detect Credit Card Frauds using Attribute Selection and Ensemble Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2018},
	volume = {180},
	number = {21},
	month = {Feb},
	year = {2018},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume180/number21/29053-2018916482},
	doi = {10.5120/ijca2018916482},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Managing of an account part is an essential area in our present day era where practically every human needs to manage the bank either physically or on the web Credit-card fraud prompts billions of dollars in misfortunes for online shippers. With the advancement of machine learning calculations, analysts have been finding progressively complex ways to identify extortion, yet handy usage is infrequently detailed. In this paper we are working to identify the fraudulent accounts using classification algorithms and then to improve the accuracy of results using feature selection technique. Bee search and genetic algorithms has been used to select relevant features from large dataset. The reduced dataset has been studied for different aspects. The ensemble learning techniques are implemented to reduce the variance. The impact of bagging, stacking and voting present the optimal technique for fraud detection.

References

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Keywords

Data mining attribute selection, classification, Ensemble techniques.