Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers
Decision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks cust...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
University of Tehran
2010-03-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_20908_b51005cfab527c718ddf47657cc610d9.pdf |
| _version_ | 1811248477447913472 |
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| author | Mahmood i Alborz Mohammad Ebrahim Mohammad Pourzarandi mohammad khanbabaei |
| author_facet | Mahmood i Alborz Mohammad Ebrahim Mohammad Pourzarandi mohammad khanbabaei |
| author_sort | Mahmood i Alborz |
| collection | DOAJ |
| description | Decision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. Genetic algorithm can help in credit scoring of customers by choosing appropriate features and building optimum decision trees. Development process in pattern recognition and CRISP process are used in credit scoring of customers in construction of this model. The proposed classification model is based on clustering, feature selection, decision trees and genetic algorithm techniques. This model select and combine the best decision tree based on the optimality criteria and constructs the final decision tree for credit scoring of customers. Results show that the accuracy of proposed classification model is more than almost the entire decision tree models compared in this paper. Also the number of leaves and the size of decision tree i.e. its complexity is less than the other models. |
| first_indexed | 2024-04-12T15:28:46Z |
| format | Article |
| id | doaj.art-0706b46eadd34287ad660b88b05de5bd |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-04-12T15:28:46Z |
| publishDate | 2010-03-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-0706b46eadd34287ad660b88b05de5bd2022-12-22T03:27:10ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592010-03-012420908Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks CustomersMahmood i Alborz0Mohammad Ebrahim Mohammad Pourzarandi1mohammad khanbabaei2واحد علوم تحقیقاتدانشگاه آزاد تهران مرکزواحد علوم و تحقیقاتDecision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. Genetic algorithm can help in credit scoring of customers by choosing appropriate features and building optimum decision trees. Development process in pattern recognition and CRISP process are used in credit scoring of customers in construction of this model. The proposed classification model is based on clustering, feature selection, decision trees and genetic algorithm techniques. This model select and combine the best decision tree based on the optimality criteria and constructs the final decision tree for credit scoring of customers. Results show that the accuracy of proposed classification model is more than almost the entire decision tree models compared in this paper. Also the number of leaves and the size of decision tree i.e. its complexity is less than the other models.https://jitm.ut.ac.ir/article_20908_b51005cfab527c718ddf47657cc610d9.pdfClustering.Credit scoringDecision TreesFeatures SelectionGenetic algorithm |
| spellingShingle | Mahmood i Alborz Mohammad Ebrahim Mohammad Pourzarandi mohammad khanbabaei Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers Journal of Information Technology Management Clustering. Credit scoring Decision Trees Features Selection Genetic algorithm |
| title | Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers |
| title_full | Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers |
| title_fullStr | Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers |
| title_full_unstemmed | Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers |
| title_short | Using Genetic Algorithm in Optimizing Decision Trees for Credit Scoring of Banks Customers |
| title_sort | using genetic algorithm in optimizing decision trees for credit scoring of banks customers |
| topic | Clustering. Credit scoring Decision Trees Features Selection Genetic algorithm |
| url | https://jitm.ut.ac.ir/article_20908_b51005cfab527c718ddf47657cc610d9.pdf |
| work_keys_str_mv | AT mahmoodialborz usinggeneticalgorithminoptimizingdecisiontreesforcreditscoringofbankscustomers AT mohammadebrahimmohammadpourzarandi usinggeneticalgorithminoptimizingdecisiontreesforcreditscoringofbankscustomers AT mohammadkhanbabaei usinggeneticalgorithminoptimizingdecisiontreesforcreditscoringofbankscustomers |