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Classifier Performances for Credit Risk Analysis: a Hybrid Classification Approach on Credit Risk Analysis Erkan Cetiner
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Classifier Performances for Credit Risk Analysis: a Hybrid Classification Approach on Credit Risk Analysis
Erkan Cetiner
This work is prepared for a Master Research Thesis. The main objective of the work is gathering single classification techniques together as one unique hybrid classifier. Experiments made on different data-sets and results are compared in terms of accuracy and precision. Logistic regression, support vector machines, artificial neural networks and naive bayes approach are examined throughout the research. A hybrid model based on average weighting mechanism developed by using those single classifiers.
| Mediji | Grāmatas Paperback Book (Grāmata ar mīksto vāku un līmēto muguru) |
| Izlaists | 2012. gada 21. aprīlis |
| ISBN13 | 9783848482030 |
| Izdevēji | LAP LAMBERT Academic Publishing |
| Lapas | 72 |
| Izmēri | 150 × 4 × 226 mm · 125 g |
| Valoda | Vācu |
Skatīt visus Erkan Cetiner ( piem., Paperback Book )