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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shin-Ying Huang Rua-Huan Tsaih |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Management Information Systems, National Chengchi University, Taipei, Taiwan (Shin-Ying Huang; Rua-Huan Tsaih) |
| Abstract | The competitive learning nature of the Growing Hierarchical Self-Organizing Map (GHSOM), which is an unsupervised neural networks extended from Self-Organizing Map (SOM), can work as a regularity detector that is supposed to help discover statistically salient features of the sample population. With the spatial correspondent assumption, this study presents a prediction approach in which GHSOM is used to help identify the fraud counterpart of each non-fraud subgroup and vice versa. In this study, two GHSOMs— a non-fraud tree (NFT) and a fraud tree (FT) are generated via the non-fraud samples and the fraud samples, respectively. Each (fraud or non-fraud) training sample is classified into its belonging leaf nodes of NFT and FT. Then, two classification rules are tuned based upon all training samples to determine the associated discrimination boundary within each leaf node, and the rule with better classification performance is chosen as the prediction rule. With the spatial correspondent assumption, the prediction rule derived from such an integration of FT and NFT classification mechanisms should work well. This study sets up the experiment of fraudulent financial reporting (FFR), a sub-field of financial fraud detection (FFD), to justify the effectiveness of the proposed prediction approach and the result is quite acceptable. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 1006337 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252479 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Vectors Neural networks Data mining Predictive models Euclidean distance Security Financial Fraud Detection Fraudulent Financial Reporting Growing Hierarchical Self-Organizing Map Unsupervised Neural Networks Classification |
| Content Type | Text |
| Resource Type | Article |
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