NDLI: Bankruptcy prediction with least squares support vector machine classifiers

Content Provider	IEEE Xplore Digital Library
Author	Van Gestel, T. Baesens, B. Suykens, J. Espinoza, M. Baestaens, D.-E. Vanthienen, J. De Moor, B.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Katholieke Univ., Leuven, Belgium (Van Gestel, T.; Baesens, B.; Suykens, J.; Espinoza, M.)
Abstract	Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e.g., solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercer's theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands.
Sponsorship	IEEE Neural Networks Soc. (NNS)
Starting Page	1
Ending Page	8
File Size	855318
Page Count	8
File Format	PDF
ISBN	0780376544
DOI	10.1109/CIFER.2003.1196234
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares methods Support vector machines Support vector machine classification Kernel Predictive models Classification algorithms Linear discriminant analysis Logistics Input variables Risk analysis
Content Type	Text
Resource Type	Article

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