Inducing Relational Fuzzy Classification Rules by Means of Cooperative Coevolution

2009 ◽  
pp. 127-147
Author(s):  
Vahab Akbarzadeh ◽  
Alireza Sadeghian ◽  
Marcus V. dos Santos
Keyword(s):  
Fuzzy Classification ◽  
Classification Rules ◽  
Cooperative Coevolution

Learning Fuzzy Classification Rules from Data

2001 ◽  
pp. 108-115 ◽  
Author(s):  
Hans Roubos ◽  
Magne Setnes ◽  
Janos Abonyi
Keyword(s):  
Fuzzy Classification ◽  
Classification Rules

Fuzzy Credit Risk Scoring Rules using FRvarPSO

2018 ◽  
Vol 26 (Suppl. 1) ◽  
pp. 39-57 ◽  
Author(s):  
Patricia Jimbo Santana ◽  
Laura Lanzarini ◽  
Aurelio F. Bariviera
Keyword(s):  
Fuzzy Logic ◽  
Selection Process ◽  
Credit Scoring ◽  
Optimization Technique ◽  
Fuzzy Classification ◽  
Future Research ◽  
Classification Rules ◽  
Risk Scoring ◽  
Variable Population ◽  
Good Risk

There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador — one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.

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