Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2017-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_788_d463e5ab58d0ee61111b6ced57755c5f.pdf |
| _version_ | 1818892572099084288 |
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| author | F. Fadaei Noghani M. Moattar |
| author_facet | F. Fadaei Noghani M. Moattar |
| author_sort | F. Fadaei Noghani |
| collection | DOAJ |
| description | Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification, using cost sensitive decision trees is performed in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The proposed method is assessed using accuracy, recall, and F-measure as evaluation metrics and compared with basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network and NB tree. Experiments show that considering the F-measure as evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to other classifiers. |
| first_indexed | 2024-12-19T17:58:50Z |
| format | Article |
| id | doaj.art-b425a9c0c1454d84ab3a74dde7a45cf6 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-19T17:58:50Z |
| publishDate | 2017-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-b425a9c0c1454d84ab3a74dde7a45cf62022-12-21T20:11:44ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442017-07-015223524310.22044/jadm.2016.788788Ensemble Classification and Extended Feature Selection for Credit Card Fraud DetectionF. Fadaei Noghani0M. Moattar1Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification, using cost sensitive decision trees is performed in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The proposed method is assessed using accuracy, recall, and F-measure as evaluation metrics and compared with basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network and NB tree. Experiments show that considering the F-measure as evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to other classifiers.http://jad.shahroodut.ac.ir/article_788_d463e5ab58d0ee61111b6ced57755c5f.pdfcredit card fraud detectionFeature Selectionensemble classificationcost sensitive learning |
| spellingShingle | F. Fadaei Noghani M. Moattar Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection Journal of Artificial Intelligence and Data Mining credit card fraud detection Feature Selection ensemble classification cost sensitive learning |
| title | Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection |
| title_full | Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection |
| title_fullStr | Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection |
| title_full_unstemmed | Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection |
| title_short | Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection |
| title_sort | ensemble classification and extended feature selection for credit card fraud detection |
| topic | credit card fraud detection Feature Selection ensemble classification cost sensitive learning |
| url | http://jad.shahroodut.ac.ir/article_788_d463e5ab58d0ee61111b6ced57755c5f.pdf |
| work_keys_str_mv | AT ffadaeinoghani ensembleclassificationandextendedfeatureselectionforcreditcardfrauddetection AT mmoattar ensembleclassificationandextendedfeatureselectionforcreditcardfrauddetection |