Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1504/IJDMMM.2019.098969 | INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT |
Keywords | Field | DocType |
credit risk modelling, data mining, sampling, classification | Financial risk management,Credit risk assessment,Data mining,Computer science,Algorithm,Credit rating,Sampling (statistics),Artificial intelligence,Predictive modelling,Sample selection,Machine learning,Credit risk | Journal |
Volume | Issue | ISSN |
11 | 2 | 1759-1163 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eftychios Protopapadakis | 1 | 149 | 22.38 |
| Dimitrios Niklis | 2 | 7 | 1.48 |
| Michalis Doumpos | 3 | 4 | 2.83 |
| Anastasios D. Doulamis | 4 | 883 | 93.64 |
| Constantin Zopounidis | 5 | 1066 | 90.47 |