A hybrid approach of DEA, rough set and support vector machines for business failure prediction

被引:122
作者
Yeh, Ching-Chiang [2 ]
Chi, Der-Jang [1 ]
Hsu, Ming-Fu [3 ]
机构
[1] Chinese Culture Univ, Dept Accounting, Taipei 11114, Taiwan
[2] Natl Taipei Coll Business, Dept Business Adm, Taipei, Taiwan
[3] Natl Chi Nan Univ, Dept Int Business Studies, Taipei, Nantou County, Taiwan
关键词
Business failure; Financial ratios; DEA; Rough set; Support vector machines; DATA ENVELOPMENT ANALYSIS; NEURAL-NETWORKS; BANKRUPTCY PREDICTION; DISCRIMINANT-ANALYSIS; EFFICIENCY; MODELS;
D O I
10.1016/j.eswa.2009.06.088
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The predict ion Of business failure is an important and challenging issue that has served as the impetus for many academic Studies over the past three decades. While the efficiency of a corporation's management is generally acknowledged to be a key contributor to corporation's bankrupt, it is usually excluded from early prediction models. The objective of the study is to use efficiency as predictive variables with a proposed novel model to integrate rough set theory (RST) with support vector machines (SVM) technique to increase the accuracy of the prediction of business failure. In the proposed method (RST-SVM), data envelopment analysis (DEA) is employed as a tool to evaluate the input/output efficiency Furthermore, by RST approach, the redundant attributes in multi-attribute information table can be reduced, which showed that the number of independent variables was reduced with no information loss, is utilized as a preprocessor to improve business failure prediction capability by SVM The effectiveness of the methodology was verified by experiments comparing back-propagation neural networks (BPN) approach with the hybrid appiciach (RST-BIIN) The results shows that DEA do provide valuable information in business failure predictions and the proposed RST-SVM model provides better classification results than RST-BPN model, no matter when only considering financial ratios or the model including both financial ratios and DEA (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1535 / 1541
页数:7
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