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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Feng-Chia Wang Peng-Kai Yeh Li-Lon |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Industry and Engineering Tsing Hwa University, Taiwan (Yeh Li-Lon) || Department of Information Management at Jen Teh junior College Taiwan (Li Feng-Chia) || Department of Information Management, Hwa Hsia College, Taiwan (Wang Peng-Kai) |
| Abstract | The credit scoring has been regarded as a critical topic and its related departments make efforts to collect huge amount of data to avoid wrong decision. An effective classificatory model will objectively help managers instead of intuitive experience. This study proposes five approaches combining with the back-propagation neural network (BPN) classifier for features selection that retains sufficient information for classification purpose. Different credit scoring models are constructed by selecting attributes with five approaches. Two UCI (University of California, Irvine) data sets are chosen to evaluate the accuracy of various hybrid-BPN models. BPN classifier combines with conventional statistical LDA, Decision tree, Rough sets theory, F-score and Gray relation approaches as features preprocessing step to optimize feature space by removing both irrelevant and redundant features. In this paper, the procedure of the proposed approaches will be described and then evaluated by their performances. The results are compared in combination with BPN classifier and nonparametric Wilcoxon signed rank test will be held to show if there is any significant difference between these models. The result in this study suggests that hybrid credit scoring approach is mostly robust and effective in finding optimal subsets and is a promising method to the fields of data mining. |
| Starting Page | 28 |
| Ending Page | 32 |
| File Size | 674734 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424485017 |
| ISSN | 2157362X |
| e-ISBN | 9781424485031 |
| e-ISBN | 9781424485024 |
| DOI | 10.1109/IEEM.2010.5674600 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computational modeling Gray relational analysis F-score Classification algorithms Data mining Decision tree Rough sets theory Accuracy Rough sets Back-propagation neural network Data models Decision trees Linear discriminate analysis |
| Content Type | Text |
| Resource Type | Article |
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