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Journal of Advances in Computer Research، جلد ۳، شماره ۳، صفحات ۵۳-۶۴
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Fuzzy Apriori Rule Extraction Using Multi-Objective Particle Swarm Optimization: The Case of Credit Scoring |
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| چکیده انگلیسی مقاله |
There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. Different support and confidence parameters generate different rule bases in apriori. Therefore Multi-objective particle swarm is used as a bio-inspired technique to search and find fuzzy support and confidence parameters, which gives the optimum rules in terms of maximum accuracy, minimum number of rules and minimum average length of rule. Australian, Germany UCI and a real Iranian commercial bank datasets is used to run the algorithm. The proposed method has shown better results compared to other classifiers. |
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| کلیدواژه‌های انگلیسی مقاله |
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| نویسندگان مقاله |
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| نشانی اینترنتی |
http://jacr.iausari.ac.ir/article_631456_e189422627014addac7de2fce3ef4453.pdf |
| فایل مقاله |
اشکال در دسترسی به فایل - ./files/site1/rds_journals/1575/article-1575-1773890.pdf |
| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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