A HYBRID BUSINESS FAILURE PREDICTION MODEL USING LOCALLY LINEAR EMBEDDING AND SUPPORT VECTOR MACHINES

Fengyi Lin, Ching Chiang Yeh () and Meng Yuan Lee
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Fengyi Lin: Department of Business Management, National Taipei University of Technology, Taipei, Taiwan.
Ching Chiang Yeh: Department of Business Administration, National Taipei College of Business, Taipei, Taiwan.
Meng Yuan Lee: Institute of Commerce Automation and Management, National Taipei University of Technology, Taipei, Taiwan.

Journal for Economic Forecasting, 2013, issue 1, 82-97

Abstract: The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature extraction, is then utilized as a preprocessor to improve business failure prediction capability by SVM. The effectiveness of the methodology was verified by comparing principal component analysis (PCA) and SVM with our proposed hybrid approach. The results show that our hybrid approach not only has the best classification rate, but also produces the lowest incidence of Type I and Type II errors, and is capable to provide on time signals for better investment and government decisions with timely warnings.

Keywords: business failure; manifold learning; locally linear embedding; support vector machines (search for similar items in EconPapers)
JEL-codes: C45 G33 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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