Application of a genetic algorithm for the credit risk assessment problem

• Autorzy: Marinaki M. , Marinakis Y. , Zopounidis C.
• Języki publikacji: EN

Abstrakty

EN

In this paper, a new procedure that utilizes a Genetic algorithm in order to solve the Feature Subset Selection problem is presented. The proposed algorithm is combined with a number of nearest neighbor based classifiers. The proposed Genetic based classification algorithm is applied for the solution of the Credit Risk Assessment Classification problem. The performance of the algorithm is tested using data from 1411 firms derived from the loan portfolio of a leading Greek Commercial Bank in order to classify the firms in different groups representing different levels of credit risk. A Comparison of the algorithm with other classification methods, such as SVM, CART is performed using these data. The algorithm is, also, compared with another metaheuristic algorithm. In this algorithm, the feature subset selection problem is solved using Tabu Search and in the classification phase the Nearest Neighbor Classifier is used. The results obtained using the genetic algorithm for the credit risk assessment classification problem are better than the results of all other classification methods and the metaheuristic algorithm used for the comparisons in this paper.

Słowa kluczowe

EN

genetic algorithm; feature selection; credit risk assessment; nearest neighbour classifier

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2007
• Tom: Vol. 32, No. 2
• Strony: 139--152
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Marinaki M.

autor

Marinakis Y.

autor

Zopounidis C.

• Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania,

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