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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Goletsis, Y. | en |
| dc.date.accessioned | 2015-11-24T17:05:04Z | - |
| dc.date.available | 2015-11-24T17:05:04Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11261 | - |
| dc.rights | Default Licence | - |
| dc.subject | credit scoring, biologically inspired algorithms, ant colony optimisation, ACO, artificial immune systems, particle swarm optimisation, PSO, classification, neural networks, ANNs, support vector machines, SVM, decision trees, logit, QDA, credit worthine | en |
| dc.title | Bio-inspired intelligence for credit scoring | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | 10.1504/IJFMD.2011.038527 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημών | el |
| heal.abstract | The application of quantitative techniques for the determination of credit worthiness, i.e., the credit scoring, is a major research field for bankers and academics as it can bring about significant savings to finance institutions whilst minimising their exposure to risk. In the current work, the applicability of recent developments in machine learning techniques is examined; specifically biologically inspired techniques mimicking natural ants, bird flocking and immune system cells are applied. Experimental results are presented on three real world credit scoring datasets. Comparative results with commonly used artificial intelligence and statistical classifiers verify the suitability of the newly examined methods. | en |
| heal.journalName | International journal of financial markets and derivatives | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΟΕ | |
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