A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention
Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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Transcript of Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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DECISION TREE INDUCTION FORFINANCIAL FRAUD DETECTION USING
ENSEMBLE LEARNING TECHNIQUES
Vijayalakshmi Mahanra Ra !
"ash#an$ %rasa& Sin'h
Fa()l$y * Cm+)$in' an& In*rma$i(sM)l$im,&ia Uni-,rsi$y! Cy.,rjaya! Malaysia
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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ABSTRACT
Credit card fraud is a serious and major growing problem
in banking industries. With the advent of the rise of many
web services provided by banks, banking frauds are also
on the rise. Banking systems always have a strong security
system in order to detect and prevent fraudulent
activities of any kind of transactions. Though totally
eliminating banking fraud is almost impossible, but we canhowever minimize the frauds and prevent them from
happening by machine learning techniues. This paper
aims to conduct e"periments to study banking frauds using
ensemble tree learning techniues and genetic algorithm
to induct ensemble of decision trees on bank transactiondatasets for identifying and preventing bank fraud. #t also
provides an evaluation and effectiveness of the ensemble
of decision trees on the credit card dataset.
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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MAIN %OINTS IN ABSTRACT
minimize the frauds and prevent themfrom happening by machine learning
techniues
conduct e"periments to study banking
frauds using ensemble tree learningtechniues and genetic algorithm to
induct ensemble of decision trees
evaluation and effectiveness of theensemble of decision trees on the credit
card dataset
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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OUTLINE
$bstract %ain &oints in $bstract
%ethods
%otivation for 'sing (enetic $lgorithm
with )ecision tree #nduction algorithm
*C+.- $daBoost.%/
)ataset &arameters
0"periment 1esults Conclusion
Contact
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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METHODS
)ecision tree 2 #)3, C+. $daBoost.%/
(enetic $lgorithm *($-
W04$
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MOTIVATION FOR USING GENETIC ALGORITHM
#)3 and C+. uses greedy approach in
attribute selection
0"periment conducted to evaluate ($ as an
approach to attribute selection without using#)3 and C+.5s approach.
$lso pruning of the tree will not be reuired
using ($, as the best attribute has been
selected
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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DATASET / %ARAMETERS
(erman Credit Card $pplication 2/666 instances, 76 attributes, with
class / *good- and 7 *bad-
&arameters 8 &ercentage 9plit :6;,
Boosting with /66 iterations,
&opulation size of 6
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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E0%ERIMENT / RESULTS 123
Construct a decision tree which is improved
with the use of (enetic $lgorithm *($- for
feature selection
)ecision tree will be induced using <+= aswell as #)3 algorithm available in W04$ for
comparison
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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E0%ERIMENT / RESULTS 143
Three forms of e"periment that have
been performed are8
/- 0"periment of decision tree without
any boosting.7- 0"periment of decision tree together
with $daBoost.%/
3-0"periment of decision tree withfeature subset selection *wrapper
approach-
7/21/2019 Decision Tree Induction for Financial Fraud Detection Using Ensemble Learning Techniques
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E0%ERIMENT / RESULTS 153
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E0%ERIMENT / RESULTS 163
0"perimental results have shown that ($
with #)3 or C+. performed better
compared to using the #)3 and C+.
classifier alone C+. with $daBoost.%/ gives higher
accuracy compared to others
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CONCLUSION
)ata analytics has been done on the usage
of decision trees combined with boosting
and genetic algorithm
#mprovement in classification accuracy isobserved using boosting algorithm on
decision tree and ($ with decision tree.
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REFERENCES
9hen, $ihua, Tong, 1encheng, )eng, >aochen*766:-. $pplication of Classification %odels
on Credit Card ?raud )etection. #000
4ohavi, 1., <ohn, @.(. */AA-. Wrappers for
feature subset selection >ang, <., @onavar. */AA:-. ?eature 9ubset
9election 'sing $ (enetic $lgorithm
>oav ?reund, 1obert 0. 9chapire8
0"periments with a new boosting algorithm.#n8 Thirteenth #nternational Conference on
%achine Dearning, 9an ?rancisco, /+=E/,
/AA
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CONTACT
ijayalakshmi %ahanra 1ao
lakshmi.mahanraFgmail.com
&rof. >ashwant &rasad 9ingh
y.p.singhFmmu.edu.my
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Thank you!
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