Support vector machines in ordinal classification: an application to corporate credit scoring

Title:

Support vector machines in ordinal classification: an application to corporate credit scoring

Creator:

Dikkers, Harmen and Rothkrantz, Leon

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:3a571bfb-5cfc-41f5-8bfc-d139cc2ddafb
uuid:3a571bfb-5cfc-41f5-8bfc-d139cc2ddafb

Subject:

Credit rating analysis, bond rating prediction, ordinal classification, support vector machines, and least squares support vector machines

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Risk assessment of credit portfolios is of pivotal importance in the banking industry. The bank that has the most accurate view of its credit risk will be the most profitable. One of the main pillars in the assessing credit risk is the estimated probability of default of each counterparty, ie the probability that a counterparty cannot meet its payment obligations in the horizon of one year. A credit rating system takes several characteristics of a counterparty as inputs and assigns this counterparty to a rating class. In essence, this system is a classifier whose classes lie on an ordinal scale. In this paper we apply linear regression ordinal logistic regression, and support vector machine techniques to the credit rating problem. The latter technique is a relatively new machine learning technique that was originally designed for the two-class problem. We propose two new techniques that incorporate the ordinal character of the credit rating problem into support vector machines. The results of our newly introduced techniques are promising.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:6

Harvested from:

CDK

Metadata only:

false