A NOVEL HYBRID LEARNING APPROACH TO TRAIN HIERARCHICAL MIXTURE OF NEURAL NETWORKS MODEL FOR CREDIT SCORING IN BANK INDUSTRY

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

DADMOHAMADI DANIAL; AHMADI ABBAS

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN) Year:2017 | Volume:28 | Issue:1 Page(s): 161-174

Download Full-Text

Persian Verion

View:

969

Download:

Cites:

Information Journal Paper

Title

A NOVEL HYBRID LEARNING APPROACH TO TRAIN HIERARCHICAL MIXTURE OF NEURAL NETWORKS MODEL FOR CREDIT SCORING IN BANK INDUSTRY

Author(s)

DADMOHAMADI DANIAL | AHMADI ABBAS | Issue Writer Certificate

Keywords

BANK INDUSTRYQ1

CREDIT SCORING (CS)Q1

BINARY PARTICLE SWARM OPTIMIZATION (BPSO)Q1

MODULAR NEURAL NETWORKQ1

HIERARCHICAL MIXTURE OF NEURAL NETWORKS (HMNNS)Q1

Abstract

Credit risk in BANK INDUSTRY is the probability of non-repayment of obligations by customers at specific time. It is one of the most important hazards for banks and private institutes. Due to huge bulk of banks’ overdue receivables, establishment of a Credit Scoring (CS) system is one of the most important means of controlling such a risk. This paper uses the powerful Neural Networks in predicting and mixing them, which can classify customers in two groups of customers who pays their debts on time and customers who don’t. The used model, which has modular based structure and training, is named Hierarchical Mixture of Neural Networks (HMNN). In mentioned model, for the decomposition of problem among networks and combining results to achieve the final prediction and also the method of training of it uses new approach. The purposed approach applies Binary Particle Swarm Optimization (BPSO) for dimension reduction and decomposing the problem among modules at first, then using the modulation of the training rules specific to each module and the general training rule of this network. Results are achieved in compersion with Multi-Layer Perceptron and Laterally Connected Neural Network. Based on observed results, the suggested model could predict customers’ behaviour with punctuality.

Cites

No record.

References

No record.

Cite

APA: Copy

DADMOHAMADI, DANIAL, & AHMADI, ABBAS. (2017). A NOVEL HYBRID LEARNING APPROACH TO TRAIN HIERARCHICAL MIXTURE OF NEURAL NETWORKS MODEL FOR CREDIT SCORING IN BANK INDUSTRY. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), 28(1 ), 161-174. SID. https://sid.ir/paper/65626/en

Vancouver: Copy

DADMOHAMADI DANIAL, AHMADI ABBAS. A NOVEL HYBRID LEARNING APPROACH TO TRAIN HIERARCHICAL MIXTURE OF NEURAL NETWORKS MODEL FOR CREDIT SCORING IN BANK INDUSTRY. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)[Internet]. 2017;28(1 ):161-174. Available from: https://sid.ir/paper/65626/en

IEEE: Copy

DANIAL DADMOHAMADI, and ABBAS AHMADI, “A NOVEL HYBRID LEARNING APPROACH TO TRAIN HIERARCHICAL MIXTURE OF NEURAL NETWORKS MODEL FOR CREDIT SCORING IN BANK INDUSTRY,” INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), vol. 28, no. 1 , pp. 161–174, 2017, [Online]. Available: https://sid.ir/paper/65626/en

Related Journal Papers