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Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card

S Sharath1 , Sherwin Sampath Doddamani2 , Lakshmikantha S3

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-15 , Page no. 217-219, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si15.217219

Online published on May 16, 2019

Copyright © S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S, “Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.217-219, 2019.

MLA Style Citation: S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S "Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card." International Journal of Computer Sciences and Engineering 07.15 (2019): 217-219.

APA Style Citation: S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S, (2019). Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card. International Journal of Computer Sciences and Engineering, 07(15), 217-219.

BibTex Style Citation:
@article{Sharath_2019,
author = {S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S},
title = {Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {15},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {217-219},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1230},
doi = {https://doi.org/10.26438/ijcse/v7i15.217219}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i15.217219}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1230
TI - Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card
T2 - International Journal of Computer Sciences and Engineering
AU - S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S
PY - 2019
DA - 2019/05/16
PB - IJCSE, Indore, INDIA
SP - 217-219
IS - 15
VL - 07
SN - 2347-2693
ER -

           

Abstract

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to con dentiality issues. Nowadays digitalization gaining popularity because of seamless, easy and convenience use of e-commerce. It became very rampant and easy mode of payment. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards. Inspired by the recent novel idea of Trerngad [1], we also quantize the released gradients to ternary levels {−B, 0, B}, where B is the bound of gradient clipping. Voting based prediction aggregation provides the final predictions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. Capsule Network (CapsNet) is adopted to further dig some deep features on the base of the expanded features, and then a fraud detection model is trained to identify if a transaction is legal or fraud.

Key-Words / Index Term

AdaBoost, classification, credit card, fraud detection, predictive modelling, voting

References

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