Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

Fernando Mendes; João Duarte; Armando Vieira; António Gaspar-Cunha

doi:10.1007/978-3-642-11282-9_12

Soft Computing in Industrial Applications pp 109-115

Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

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Authors and affiliations

Fernando Mendes
João Duarte
Armando Vieira
António Gaspar-Cunha

Conference paper

DOI: 10.1007/978-3-642-11282-9_12

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 75)

Cite this paper as:: Mendes F., Duarte J., Vieira A., Gaspar-Cunha A. (2010) Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach. In: Gao XZ., Gaspar-Cunha A., Köppen M., Schaefer G., Wang J. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 75. Springer, Berlin, Heidelberg

Abstract

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – is fully analyzed using two classifiers: Support Vector Machines and Logistic Function. A database containing financial statements of 1200 medium sized private French companies was used. It was shown that MOEA is a very efficient feature selection approach. Furthermore, it can provide very useful information for the decision maker in characterizing the financial health of a company.

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Authors and Affiliations

Fernando Mendes
- 1
João Duarte
- 2
Armando Vieira
- 2
António Gaspar-Cunha
- 1

1.IPC/I3N - Institute of Polymers and CompositesUniversity of MinhoGuimarãesPortugal
2.Department of PhysicsInstituto Superior de Engenharia do PortoPortoPortugal

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Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

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