A Semi-deterministic Ensemble Strategy For
Imbalanced Datasets (SDEID) Applied To
Bankruptcy Prediction
Author(s)
R. A. Mathiasi Horta, B. S. L. Pires de Lima & C. C. H. Borges
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.
Keywords: data mining, bankruptcy prediction, ensemble, attribute selection,
data pre-processing.
1 Introduction
The problem of efficient bankruptcy prognosis is of great interest both to
scientists and practitioners. Owners, managers, investors, creditors and business
Keywords
data mining, bankruptcy prediction, ensemble, attribute selection,
data pre-processing.
Related Book
Other papers in this volume
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