dc.contributor.author |
Moepya, SO
|
|
dc.contributor.author |
Akhoury, SS
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|
dc.contributor.author |
Nelwamondo, Fulufhelo V
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|
dc.date.accessioned |
2015-08-19T10:56:18Z |
|
dc.date.available |
2015-08-19T10:56:18Z |
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dc.date.issued |
2014-12 |
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dc.identifier.citation |
Moepya, S.O, Akhoury, S.S and Nelwamondo, F.V. 2014. Applying cost-sensitive classification for financial fraud detection under high class-imbalance. In: 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, 14 December 2014 |
en_US |
dc.identifier.uri |
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7022596
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|
dc.identifier.uri |
http://hdl.handle.net/10204/8067
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dc.description |
2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, 14 December 2014. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website |
en_US |
dc.description.abstract |
In recent years, data mining techniques have been used to identify companies who issue fraudulent financial statements. However, most of the research conducted thus far use datasets that are balanced. This does not always represent reality, especially in fraud applications. In this paper, we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy, sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Workflow;14830 |
|
dc.subject |
Financial statement fraud |
en_US |
dc.subject |
Data mining |
en_US |
dc.subject |
High class-imbalance |
en_US |
dc.subject |
Cost-sensitive classification |
en_US |
dc.title |
Applying cost-sensitive classification for financial fraud detection under high class-imbalance |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Moepya, S., Akhoury, S., & Nelwamondo, F. V. (2014). Applying cost-sensitive classification for financial fraud detection under high class-imbalance. IEEE. http://hdl.handle.net/10204/8067 |
en_ZA |
dc.identifier.chicagocitation |
Moepya, SO, SS Akhoury, and Fulufhelo V Nelwamondo. "Applying cost-sensitive classification for financial fraud detection under high class-imbalance." (2014): http://hdl.handle.net/10204/8067 |
en_ZA |
dc.identifier.vancouvercitation |
Moepya S, Akhoury S, Nelwamondo FV, Applying cost-sensitive classification for financial fraud detection under high class-imbalance; IEEE; 2014. http://hdl.handle.net/10204/8067 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Moepya, SO
AU - Akhoury, SS
AU - Nelwamondo, Fulufhelo V
AB - In recent years, data mining techniques have been used to identify companies who issue fraudulent financial statements. However, most of the research conducted thus far use datasets that are balanced. This does not always represent reality, especially in fraud applications. In this paper, we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy, sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers.
DA - 2014-12
DB - ResearchSpace
DP - CSIR
KW - Financial statement fraud
KW - Data mining
KW - High class-imbalance
KW - Cost-sensitive classification
LK - https://researchspace.csir.co.za
PY - 2014
T1 - Applying cost-sensitive classification for financial fraud detection under high class-imbalance
TI - Applying cost-sensitive classification for financial fraud detection under high class-imbalance
UR - http://hdl.handle.net/10204/8067
ER -
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en_ZA |