基于邻域粗糙集的企业财务危机预警指标选择被引量：19

Index Selection of Enterprise's Financial Crisis Early Warning Based on Neighborhood Rough Set

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摘要财务危机预警由于其在经济管理实践中的重要性,日益受到广泛关注。就财务危机预警问题而言,首要问题是如何从大量备选指标中科学地选择预警指标。本文在对已有研究进行分析的基础上,应用邻域粗糙集模型对预警备选指标进行属性约简,得到利益相关者重点观测和预警建模所需的预警指标。研究结果显示,邻域粗糙集约简明显优于经典粗糙集约简,能够以较少的特征变量实现较高的分类精度。 Financial Crisis early Warning has increasingly attracted the extensive attention due to its importance in the economic management practice. On the questions of the financial crisis early warning, the first question is how to select the early warning index from a large number of alternative indexes scientifi- cally. Based on the analysis of existed researches, this paper applies the neighborhood rough set model for attribute reduction of early warning alternative indexes, and gets the early warning index observed by stakeholders and needed by modeling of early warning. The result suggests that the neighborhood rough set model is better than the classic rough set model, and it can give a higher classification accuracy by a fewer characteristics variables.

作者宋鹏梁吉业曹付元

机构地区山西大学管理学院山西大学智能信息处理研究所

出处《经济管理》 CSSCI 北大核心 2009年第8期130-135,共6页 Business and Management Journal ( BMJ )

基金国家自然科学基金项目"复杂信息系统的粒度结构与知识获取研究"(60773133) 山西省社科研究规划项目"山西省上市公司财务危机预警实证研究"(0705102) 山西大学校人文社科基金项目"企业财务危机预警的实证分析"(0609020)

关键词邻域粗糙集财务危机预警 10折交叉验证 neighborhood rough set financial crisis early warning 10 - fold cross validation

分类号 F272.1 [经济管理—企业管理]

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1Beynon M. J. , Peel M. J. Variable Precision Rough Set Theory and Data Discretisation: an Application to Corporate Failure Prediction [J]. OMEGA, 2001, 29,(6): 561--576.
2Edward I. Altman. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy[J]. Journal of Finance, 1968, 23, (4) :589--609.
3Edward I. Altman, Marco G, Varettl F. Corporate Distress Diagnosis:Comparisons Using Linear Discriminant Analysis and Neural Networks[J]. Journal of Banking and Finance, 1994, 18, (3) : 505--529.
4Hu, Q. et al. Neighborhood Classifiers[J]. Expert Systems with Applications, 2008, (34): 866--876.
5Jiye. Liang, K. S. Chin, C. Y. Dang and C. M. YAM. Richard. A New Method for Measuring Uncertainty and Fuzziness in Rough Set Theory[J]. International Journal of General Systems, 2002,31, (4) : 331 -- 342.
6James A. Ohlson. Financial Ratios and the Probabilistic Prediction of Bankruptcy[J]. Journal of Accounting Research, 1980, 18, (1): 109--131.
7Jiye. Liang, Zhongzhi. Shi, Deyu. Li and M. J. Wierman. Information Entropy, Rough Entropy and Knowledge Granulation in Incomplete Information Systems[J]. International Journal of General Systems, 2006, 34, (1) : 641--654.
8Lin T. Y. Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems[C]. In: Skoworn A, Polkowshi L, eds. In: Proc. Of the Rough Sets in Knowledge Discovery. Physica-Verlag, 1998, 107--121.
9Paul J. Fitz Patrick. A Comparison of Ratios of Successful Industrial Enterprises with those of Failed Firms[J]. Certified Public Accountant, October, November, December, 1932:598--605, 656--662, 727--731.
10PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982, 11, (5):341--356.