Name
Abstract | ||
|---|---|---|
In this paper we study personal credit scoring using several machine learning algorithms: Multilayer Perceptron, Logistic Regression, Support Vector Machines, AddaboostM1 and Hidden Layer Learning Vector Quantization. The scoring models were tested on a large dataset from a Portuguese bank. Results are benchmarked against traditional methods under consideration for commercial applications. A measure of the usefulness of a scoring model is presented and we show that HLVQ-C is the most accurate model. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-03040-6_12 | Advances in Neuro-Information Processing |
Keywords | Field | DocType |
accurate model,support vector machines,improving personal credit scoring,personal credit,portuguese bank,multilayer perceptron,scoring model,logistic regression,hidden layer learning vector,commercial application,large dataset,support vector machine,learning vector quantization,machine learning | Structured support vector machine,Data mining,Feature selection,Computer science,Multilayer perceptron,Artificial intelligence,Credit risk,Pattern recognition,Learning vector quantization,Support vector machine,Relevance vector machine,Linear discriminant analysis,Machine learning | Conference |
Volume | ISSN | Citations |
5507 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armando Vieira | 1 | 147 | 11.48 |
| João Duarte | 2 | 67 | 5.10 |
| Bernardete Ribeiro | 3 | 758 | 82.07 |
| Joao Carvalho Neves | 4 | 0 | 0.34 |