Name
Abstract | ||
|---|---|---|
This article presents a study on development of credit risk evaluation model using Support Vector Machines based classifiers, such as linear SVM, stochastic gradient descent based SVM, LibSVM, Core Vector Machines (CVM), Ball Vector Machines (BVM) and other. Discriminant analysis was applied for evaluation of financial instances and dynamic formation of bankruptcy classes. The possibilities of feature selection application were also researched by applying correlation-based feature subset evaluator and Tabu search. This research showed that different SVM classifiers produced similar results, including Core Vector Machines based classifier. Yet proper selection of classifier and its parameters remains an important problem. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.procs.2011.04.184 | PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS) |
Keywords | Field | DocType |
Support Vector Machines, SVM, Core Vector Machines, CVM, machine learning, credit risk, evaluation, bankruptcy, Altman | Data mining,Feature selection,Computer science,Random subspace method,Artificial intelligence,Classifier (linguistics),Stochastic gradient descent,Pattern recognition,Support vector machine,Linear discriminant analysis,Margin classifier,Tabu search,Machine learning | Journal |
Volume | ISSN | Citations |
4 | 1877-0509 | 7 |
PageRank | References | Authors |
0.56 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulius Danenas | 1 | 35 | 5.07 |
| Gintautas Garsva | 2 | 41 | 4.95 |
| Saulius Gudas | 3 | 42 | 7.82 |