Neuro-Fuzzy Classifiers for Credit Scoring

被引：0

作者：

Constantinescu, Alina ^{[1
]}

Badea, Leonardo ^{[2
]}

Cucui, Ion ^{[2
]}

Ceausu, George ^{[2
]}

机构：

[1] Univ Valahia Targoviste, Fac Sci & Arts, Blv Carol 1, 2, Targoviste, Dambovita, Romania

[2] Univ Valahia Targoviste, Fac Econ & Sci, Targoviste, Dambovita, Romania

来源：

RECENT ADVANCES IN MANAGEMENT, MARKETING, FINANCES: PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE (MMF 10) | 2010年

关键词：

Artificial learning; Neuro-fuzzy classifier; Credit scoring; RECOGNITION; NETWORKS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The credit scoring is a classification problem in which data from application form for new or extended credit are used in order to assign credit applicants to 'good' or 'bad' credit risk classes. Recently, economics and finance researchers take into account the artificial learning and statistical pattern recognition techniques for building credit modeling systems. Starting from a neuro-fuzzy classifier (NEFC) proposed by Taur and Tao, in this paper we design a new classification model for credit scoring. Our approach considers the cost functions in the inductive construction of classifier.

引用

页码：132 / +

页数：2

共 13 条

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