A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic

被引：5
|
作者：
Latinovic, Milica [1 ]
Dragovic, Ivana [2 ]
Arsic, Vesna Bogojevic [1 ]
Petrovic, Bratislav [2 ]
机构：
[1] Univ Belgrade, Fac Org Sci, Dept Financial Management & Accounting, Jove Ilica 154, Belgrade 11000, Serbia
[2] Univ Belgrade, Fac Org Sci, Dept Syst Theory & Control, Jove Ilica 154, Belgrade 11000, Serbia
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2018年 / 11卷 / 01期
关键词：
Fuzzy Inference System; Boolean Consistent Fuzzy Logic; Banks; Credit Scoring; Performance; NEURAL-NETWORKS; DISCRIMINANT-ANALYSIS; REGRESSION TREE; HYBRID MODELS; CLASSIFICATION; RISK; CLASSIFIERS; ALGORITHMS; SELECTION; PERFORMANCE;
D O I：
10.2991/ijcis.11.1.31
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This study proposes implementation of Boolean consistent fuzzy inference system for credit scoring purposes. Fuzzy inference system (FIS) allows domain experts to express their knowledge in the form of fuzzy rules, which enables combination of automatic rating with human judgment. Crucial for this model is that fuzzy rules are being evaluated using Boolean consistent fuzzy logic, which preserves all Boolean axioms. Experimental results show that the Boolean consistent FIS outperforms the conventional FIS in terms of classification accuracy, precision, and recall. Consistent fuzzy logic could contribute to the rightful approval of more loans which in turn would have positive effects on economic growth.
引用
收藏
页码：414 / 427
页数：14
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