Predicting regional credit ratings using ensemble classification with metacost

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914921" target="_blank" >RIV/00216275:25410/19:39914921 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-19810-7_33" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-19810-7_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-19810-7_33" target="_blank" >10.1007/978-3-030-19810-7_33</a>

Result language

angličtina

Original language name

Predicting regional credit ratings using ensemble classification with metacost

Original language description

Ensemble classifiers are learning algorithms that combine sets of base classifiers in order to increase their diversity and, thus, decrease variance and achieve better predictive performance compared to single classifiers. Previous research has shown that ensemble classifiers are more accurate than single classifiers in predicting credit ratings. Here we deal with highly imbalanced multi-class data of regional entities. To overcome these problems, we propose a novel hybrid model combining data oversampling and cost-sensitive ensemble classification. This paper demonstrates that the use of the SMOTE technique to balance the multi-class data solves the imbalance problem effectively. Different misclassification cost assigned in cost matrix solves the problem of ordered classes. This approach is combined with ensemble classification within the MetaCost framework. We show that more accurate prediction can be achieved using this approach in terms of average cost and area under ROC. This paper provides empirical evidence on the dataset of 451 regions classified into 8 rating classes, as obtained from the Moody’s rating agency. The results show that Random Forest combined with MetaCost outperforms the rest of the base classifiers, as well as other benchmark methods.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Intelligence Methods in Intelligent Algorithms : Proceedings of 8th Computer Science On-line Conference 2019, Vol. 2

ISBN

978-3-030-19809-1

ISSN

2194-5357

e-ISSN

—

Number of pages

11

Pages from-to

332-342

Publisher name

Springer Nature

Place of publication

Heidelberg

Event location

Praha

Event date

Apr 24, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000503762800033