Work

A semi-deterministic ensemble strategy for imbalanced datasets (SDEID) applied to bankruptcy prediction

Year: 2008

Type: conference-paper

Abstract: In the last decade, there was a rapid growth in the availability and use of credit for Brazilian companies.Until recently, the decision to grant credit was based on human trial to evaluate the risk of insolvency.Increased demand from companies for credit has led to the use of more accurate models for bankruptcy prediction.In recent years much progress has occurred in the process of drawing up a model fostered by increased competition among financial institutions, changes in the economic environment for businesses and advances in computational techniques.This article discusses and presents alternatives for some of the main problems in the preparation of models for bankruptcy prediction with the application of data mining techniques.The first problem approached is the class imbalance that may cause a poor classification performance and it is treated jointly with an ensemble strategy.The other one rely on the selection of the most significant combination of attributes, the financial variables, which have been widely studied in insolvency prediction.Finally, it is presented a case study in a real world data base of Brazilian companies.

Source: WIT transactions on information and communication technologies

DOI: 10.2495/data080201

Authors: R. A. Mathiasi Horta, B. S. L. Pires de Lima, C. C. H. Borges

Institutions: Universidade Federal de Juiz de Fora, Universidade Federal do Rio de Janeiro, Laboratório Nacional de Computação Científica

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: bronze

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico

OpenAlex ID: W2071787443