Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation

DOI10.1080/00207721.2012.720293zbMath1309.91146OpenAlexW2166996073MaRDI QIDQ5172554

Jerome Yen, Kin Keung Lai, Li-Gang Zhou

Publication date: 4 February 2015

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2012.720293

zbMATH Keywords

genetic algorithmsupport vector machinesdirect searchbankruptcy prediction

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Corporate finance (dividends, real options, etc.) (91G50)

Uses Software

• LIBSVM

Cites Work

• Feature extraction. Foundations and applications. Papers from NIPS 2003 workshop on feature extraction, Whistler, BC, Canada, December 11--13, 2003. With CD-ROM.
• Bankruptcy prediction in banks and firms via statistical and intelligent techniques -- a review
• Credit scoring using support vector machines with direct search for parameters selection
• Genetic programming and rough sets: a hybrid approach to bankruptcy classification
• Extraction of classification rules characterized by ellipsoidal regions using soft-computing techniques

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