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Information Journal Paper

Title

FUZZY APRIORI RULE EXTRACTION USING MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION: THE CASE OF CREDIT SCORING

Pages

  53-64

Abstract

 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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    APA: Copy

    GHOLAMIAN, MOHAMMAD REZA, SADATRASOUL, SEYED MAHDI, & HAJIMOHAMMADI, ZEYNAB. (2012). FUZZY APRIORI RULE EXTRACTION USING MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION: THE CASE OF CREDIT SCORING. JOURNAL OF ADVANCES IN COMPUTER RESEARCH, 3(3), 53-64. SID. https://sid.ir/paper/328704/en

    Vancouver: Copy

    GHOLAMIAN MOHAMMAD REZA, SADATRASOUL SEYED MAHDI, HAJIMOHAMMADI ZEYNAB. FUZZY APRIORI RULE EXTRACTION USING MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION: THE CASE OF CREDIT SCORING. JOURNAL OF ADVANCES IN COMPUTER RESEARCH[Internet]. 2012;3(3):53-64. Available from: https://sid.ir/paper/328704/en

    IEEE: Copy

    MOHAMMAD REZA GHOLAMIAN, SEYED MAHDI SADATRASOUL, and ZEYNAB HAJIMOHAMMADI, “FUZZY APRIORI RULE EXTRACTION USING MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION: THE CASE OF CREDIT SCORING,” JOURNAL OF ADVANCES IN COMPUTER RESEARCH, vol. 3, no. 3, pp. 53–64, 2012, [Online]. Available: https://sid.ir/paper/328704/en

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