Towards a Comprehensible and Accurate Credit Management Model: Application of Four Computational Intelligence Methodologies

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Tsakonas:2006:ISEFS,

• author = "Athanasios Tsakonas and Nikolaos Ampazis and Georgios Dounias",
• title = "Towards a Comprehensible and Accurate Credit Management Model: Application of Four Computational Intelligence Methodologies",
• booktitle = "2006 International Symposium on Evolving Fuzzy Systems",
• year = "2006",
• month = sep,
• pages = "295--299",
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, applicant classification, banking, computational intelligence, credit management model, credit risk, feedforward neural networks, fuzzy rule based systems, grammar-guided genetic programming, hierarchical decision trees, inductive machine learning, rule-based categorization, second order methods, bank data processing, decision trees, feed forward neural nets, fuzzy systems, grammars, learning by example, risk management",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.7374",
• DOI = "doi:10.1109/ISEFS.2006.251142",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• contributor = "CiteSeerX",
• language = "en",
• oai = "oai:CiteSeerXPSU:10.1.1.149.7374",
• abstract = "The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feed forward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in banking",
• notes = "also known as \cite{4016706}",

}

Genetic Programming entries for Athanasios D Tsakonas Nikolaos Ampazis Georgios Dounias

Citations