Sample selection algorithms for credit risk modelling through data mining techniques

被引：7

作者：

Protopapadakis, Eftychios ^{[1
]}

Niklis, Dimitrios ^{[2
]}

Doumpos, Michalis ^{[3
]}

Doulamis, Anastasios ^{[1
]}

Zopounidis, Constantin ^{[3
,4
]}

机构：

[1] Natl Tech Univ Athens, Sch Rural & Surveying Engn, 9 Iroon Polytech Str, Zografos 15780, Greece

[2] Western Macedonia Univ Appl Sci, Dept Accounting & Finance, Koila Kozani 50100, Greece

[3] Tech Univ Crete, Sch Prod Engn & Management, Financial Engn Lab, Univ Campus, Khania 73100, Greece

[4] Audencia Business Sch, 8 Route Joneliere,BP 31222, F-44312 Nantes 3, France

来源：

INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT | 2019年 / 11卷 / 02期

关键词：

credit risk modelling; data mining; sampling; classification; CLASSIFICATION ALGORITHMS; PREDICTION;

D O I：

10.1504/IJDMMM.2019.098969

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Credit risk assessment is a very challenging and important problem in the domain of financial risk management. The development of reliable credit rating/scoring models is of paramount importance in this area. There are different algorithms and approaches for constructing such models to classify credit applicants (firms or individuals) into risk classes. Reliable sample selection is crucial for this task. The aim of this paper is to examine the effectiveness of sample selection schemes in combination with different classifiers for constructing reliable default prediction models. We consider different algorithms to select representative cases and handle class imbalances. Empirical results are reported for a dataset of Greek companies from the commercial sector.

引用

页码：103 / 128

页数：26

共 51 条

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