Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/19288

TitleFeature selection for bankruptcy prediction: a multi-objective optimization approach
Author(s)Mendes, F.
Duarte, J.
Vieira, Armando
Gaspar-Cunha, A.
KeywordsFeature selection
Bankruptcy prediction
Multi-objective optimization
Evolutionary algorithms
Support vector machines
Logistic regression
Issue date2010
PublisherSpringer
JournalAdvances in Intelligent and Soft Computing
CitationMendes 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(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/19288
ISBN978-3-642-11281-2
e-ISBN978-3-642-11282-9
DOI10.1007/978-3-642-11282-9_12
ISSN1867-5662
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-3-642-11282-9_12
Peer-Reviewedyes
AccessOpen access
Appears in Collections:IPC - Resumos alargados em actas de encontros científicos internacionais com arbitragem


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