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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

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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.

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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

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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

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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

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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

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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

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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-

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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!

Q&A