上市公司财务危机集成预警建模研究
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摘要
财务危机预警是企业财务管理和投资决策领域的重要问题,其实质是对企业未来财务状况的预报和分类,一直备受理论研究和实务研究的高度关注。企业发生财务危机将影响管理者、债权人、投资人等利益相关者的经济利益,甚至引起国家资本市场的波动和损失。在当前全球资本环境持续低迷,金融危机影响仍然存在的背景下,如何监控企业的财务风险因素,更加有效的开展财务危机预警,具有重要的现实意义。
我国财务危机预警研究起步比较晚,企业面临的内外部环境复杂多变,国外传统的财务危机预警模型难以适应我国的实际情况。因此,迫切需要探索一套合理的财务危机预警建模方法,为相关利益主体提供决策建议和技术支持。然而,目前的财务危机预警模型多数仍集中于单一模型预警,或者是几种模型的组合预警以及分类器集成的简单应用,而且建模过程中没有考虑财务数据概念漂移对预警模型的影响。鉴于已有预警模型的不足,本文以上市公司为对象进行有针对性的研究。一方面,将分类器集成及其改进方法用于财务危机预警的建模过程。另一方面,考虑企业财务数据概念漂移对预警模型的影响,建立动态更新的财务危机预警模型。
首先,分类器集成技术研究。详细解释了分类器集成的概念和原理,研究分类器集成的关键步骤,包括基分类器的生成、基分类器的选择以及基分类器的输出,介绍三种常用的分类器集成算法并进行比较和说明。针对目前已有财务危机预警模型中使用分类器集成技术的各种问题,从群体决策、样本量、方便性等方面,分析分类器集成在财务危机预警中的适用性。
其次,基于分类器集成的财务危机预警研究。已有集成预警研究只是分类器集成的简单应用,论文详细分析分类器集成系统的泛化能力,尝试从基分类器的差异度和基分类器的个体精度两种思路来提升集成系统泛化能力。一方面,从增加基分类器差异度的角度提出RS-Bagging分类器集成方法。该方法通过样本分布扰动和特征空间扰动的双重扰动策略,使基分类器能够保持更大差异度。模型实验表明,RS-Bagging预警模型能弥补单一分类器模型的不足,较大提升原有Bagging集成模型的性能。另一方面,从增加基分类器个体精度的角度,提出MTS-Bagging分类器集成方法。该方法是将马田系统特征选择加入Bagging算法,通过马田系统方法选取有效分类特征,从而提高基分类器的个体精度。模型实验表明,MTS-Bagging集成方法适合财务危机预警建模,预测精度优于原有Bagging集成模型,但低于RS-Bagging集成模型。另外,对于财务危机预警而言,发生两类错误的风险成本存在差异。以错误分类的风险成本对RS-Bagging模型和MTS-Bagging模型进行比较,发现MTS-Bagging模型的综合错误率低于其他集成模型。
再次,基于滑动时间窗口的财务危机集成预警研究。财务危机预警中的财务数据来自于不同年份,可能存在概念漂移问题。论文针对概念漂移开展研究,使用基于滑动时间窗口的样本动态更新以及马田系统的特征动态更新来处理可能的概念漂移,模拟时间推移在不同时间窗口宽度下进行集成预警建模实验。结果表明,财务数据的概念漂移确实存在,选择宽度合适的滑动时间窗口可以降低漂移概念的不利影响。
最后,基于增量学习系统的财务危机集成预警研究。鉴于已有预警模型增量学习能力的不足,论文设计了一种基于分类器集成思想的增量学习系统。利用财务类别导向知识对集成子系统实现动态选择,构建具有增量学习能力的财务危机预警模型。财务数据实验表明,基于增量学习系统的财务危机预警模型兼具稳定性和适应性,模型预测性能优于滑动时间窗口模型,是一种有效的财务危机预警建模方法。
Financial distress prediction is an important issue in corporate financial management and investment decision-making, and its essence is to forecast future financial position and classification, which has always been of great concern in theoretical and practical research. The financial distress will affect the economic interests of the managers, creditors, investors and other stakeholders, and even lead to the national capital market volatility and losses. Under the background of the current global capital environment continues to slump and the financial distress still exist, how to monitor the financial risk factors more effectively and carry out the financial distress prediction has important practical significance.
Because of the research about financial distress prediction is late in China, and the internal and external environments of the enterprise are complex and variable, foreign financial distress prediction model is difficult to adapt to the reality of our country. Therefore, there is an urgent need to explore a reasonable financial distress prediction model for the relevant stakeholders to provide policy advice and technical support. However, the majority of the current financial distress prediction model is still concentrated in a single model for prediction, or a combination of several models and simple application of classifier ensemble. The models do not consider the adverse effects of the concept drift of financial data. Given the inadequate of the existing financial distress prediction model, we conduct targeted research on listed companies. On the one hand, the classifier ensemble and its improved method have been studied for the modeling process of the financial distress prediction. On the other hand, dynamic incremental model for financial distress prediction is built, considering the concept drift of corporate financial data.
Firstly, we research on the classifier ensemble. The concepts and principles of the classifier ensemble are explained in this paper. Then the key steps of classifier ensemble technology are analyzed, mainly including the generation of the base classifiers, and the selection of classifiers and classifiers output. Three commonly used classifier ensemble algorithm are discussed and compared. For the main problem currently exists in classifier ensemble applied research, the applicability of the classifier ensemble in financial distress prediction are analyzed in detail from group decision making, sample size, convenience, etc.
Secondly, we research on the model for financial distress prediction based on the classifier ensemble. For the exsiting models are only simple application by classifier ensemble, we analyze the generalization capability of the classifier ensemble system, and use the diversity of base classifiers and individual classifier performances to promote the generalization capability of the ensemble system. On one hand, RS-Bagging classifier ensemble method was proposed by increasing the diversity of base classifiers. The essence of this method is a double disturbance strategy through disturbance of the sample and the feature space. In this way classifier can keep more difference in ensemble system. Model experiments show that, RS-Bagging ensemble prediction model can make up for the deficiency of the single classifier model. RS-Bagging ensemble model outperforms the original Bagging ensemble prediction model. On the other hand, MTS-Bagging classifier ensemble method is put forward by increasing the individual base classifier performances. Mahalanobis-Taguchi System is applied to Bagging algorithm in the method. Proper features are selected by using Mahalanobis-Taguchi System. In this way, individual classifier performances can be improved. Model experiments show that the MTS-Bagging model suitable for financial distress prediction. The accuracy of the MTS-Bagging model outperforms the original Bagging ensemble prediction model. In addition, for the financial distress prediction, the risk cost of the two type errors is different. Considering the risk cost of misclassification, the error rate of MTS-Bagging model is lower than other modles in the research.
Thirdly, we research on the model for financial distress prediction based on sliding time window technology. Because of the data samples of financial distress prediction come from different years, there may be a concept drift problem in the modeling. The concept drift problem is solved by the sliding time window and Mahalanobis-Taguchi System, including feature dynamic selection and sample dynamic selection. The prediction model experiment is carried on the different width of the sliding time window. The results show that the concept drift of the financial data does exist. Select the appropriate width of the sliding time window can reduce the adverse effects of the drift concept.
Finally, we research on the model for financial distress prediction based on the incremental learning system. Given the lack of incremental learning ability of existing prediction model, an incremental learning system based on the classifier ensemble is designed. Financial category oriented knowledge is applied to realize the dynamic selection for classifier ensemble subsystem. Then the financial distress prediction model with incremental learning ability is build. Model experiments show that, the financial distress prediction model based on the incremental learning system is stable and adaptable. Incremental prediction model outperforms other models, which is an effective financial distress prediction method.
我国财务危机预警研究起步比较晚,企业面临的内外部环境复杂多变,国外传统的财务危机预警模型难以适应我国的实际情况。因此,迫切需要探索一套合理的财务危机预警建模方法,为相关利益主体提供决策建议和技术支持。然而,目前的财务危机预警模型多数仍集中于单一模型预警,或者是几种模型的组合预警以及分类器集成的简单应用,而且建模过程中没有考虑财务数据概念漂移对预警模型的影响。鉴于已有预警模型的不足,本文以上市公司为对象进行有针对性的研究。一方面,将分类器集成及其改进方法用于财务危机预警的建模过程。另一方面,考虑企业财务数据概念漂移对预警模型的影响,建立动态更新的财务危机预警模型。
首先,分类器集成技术研究。详细解释了分类器集成的概念和原理,研究分类器集成的关键步骤,包括基分类器的生成、基分类器的选择以及基分类器的输出,介绍三种常用的分类器集成算法并进行比较和说明。针对目前已有财务危机预警模型中使用分类器集成技术的各种问题,从群体决策、样本量、方便性等方面,分析分类器集成在财务危机预警中的适用性。
其次,基于分类器集成的财务危机预警研究。已有集成预警研究只是分类器集成的简单应用,论文详细分析分类器集成系统的泛化能力,尝试从基分类器的差异度和基分类器的个体精度两种思路来提升集成系统泛化能力。一方面,从增加基分类器差异度的角度提出RS-Bagging分类器集成方法。该方法通过样本分布扰动和特征空间扰动的双重扰动策略,使基分类器能够保持更大差异度。模型实验表明,RS-Bagging预警模型能弥补单一分类器模型的不足,较大提升原有Bagging集成模型的性能。另一方面,从增加基分类器个体精度的角度,提出MTS-Bagging分类器集成方法。该方法是将马田系统特征选择加入Bagging算法,通过马田系统方法选取有效分类特征,从而提高基分类器的个体精度。模型实验表明,MTS-Bagging集成方法适合财务危机预警建模,预测精度优于原有Bagging集成模型,但低于RS-Bagging集成模型。另外,对于财务危机预警而言,发生两类错误的风险成本存在差异。以错误分类的风险成本对RS-Bagging模型和MTS-Bagging模型进行比较,发现MTS-Bagging模型的综合错误率低于其他集成模型。
再次,基于滑动时间窗口的财务危机集成预警研究。财务危机预警中的财务数据来自于不同年份,可能存在概念漂移问题。论文针对概念漂移开展研究,使用基于滑动时间窗口的样本动态更新以及马田系统的特征动态更新来处理可能的概念漂移,模拟时间推移在不同时间窗口宽度下进行集成预警建模实验。结果表明,财务数据的概念漂移确实存在,选择宽度合适的滑动时间窗口可以降低漂移概念的不利影响。
最后,基于增量学习系统的财务危机集成预警研究。鉴于已有预警模型增量学习能力的不足,论文设计了一种基于分类器集成思想的增量学习系统。利用财务类别导向知识对集成子系统实现动态选择,构建具有增量学习能力的财务危机预警模型。财务数据实验表明,基于增量学习系统的财务危机预警模型兼具稳定性和适应性,模型预测性能优于滑动时间窗口模型,是一种有效的财务危机预警建模方法。
Financial distress prediction is an important issue in corporate financial management and investment decision-making, and its essence is to forecast future financial position and classification, which has always been of great concern in theoretical and practical research. The financial distress will affect the economic interests of the managers, creditors, investors and other stakeholders, and even lead to the national capital market volatility and losses. Under the background of the current global capital environment continues to slump and the financial distress still exist, how to monitor the financial risk factors more effectively and carry out the financial distress prediction has important practical significance.
Because of the research about financial distress prediction is late in China, and the internal and external environments of the enterprise are complex and variable, foreign financial distress prediction model is difficult to adapt to the reality of our country. Therefore, there is an urgent need to explore a reasonable financial distress prediction model for the relevant stakeholders to provide policy advice and technical support. However, the majority of the current financial distress prediction model is still concentrated in a single model for prediction, or a combination of several models and simple application of classifier ensemble. The models do not consider the adverse effects of the concept drift of financial data. Given the inadequate of the existing financial distress prediction model, we conduct targeted research on listed companies. On the one hand, the classifier ensemble and its improved method have been studied for the modeling process of the financial distress prediction. On the other hand, dynamic incremental model for financial distress prediction is built, considering the concept drift of corporate financial data.
Firstly, we research on the classifier ensemble. The concepts and principles of the classifier ensemble are explained in this paper. Then the key steps of classifier ensemble technology are analyzed, mainly including the generation of the base classifiers, and the selection of classifiers and classifiers output. Three commonly used classifier ensemble algorithm are discussed and compared. For the main problem currently exists in classifier ensemble applied research, the applicability of the classifier ensemble in financial distress prediction are analyzed in detail from group decision making, sample size, convenience, etc.
Secondly, we research on the model for financial distress prediction based on the classifier ensemble. For the exsiting models are only simple application by classifier ensemble, we analyze the generalization capability of the classifier ensemble system, and use the diversity of base classifiers and individual classifier performances to promote the generalization capability of the ensemble system. On one hand, RS-Bagging classifier ensemble method was proposed by increasing the diversity of base classifiers. The essence of this method is a double disturbance strategy through disturbance of the sample and the feature space. In this way classifier can keep more difference in ensemble system. Model experiments show that, RS-Bagging ensemble prediction model can make up for the deficiency of the single classifier model. RS-Bagging ensemble model outperforms the original Bagging ensemble prediction model. On the other hand, MTS-Bagging classifier ensemble method is put forward by increasing the individual base classifier performances. Mahalanobis-Taguchi System is applied to Bagging algorithm in the method. Proper features are selected by using Mahalanobis-Taguchi System. In this way, individual classifier performances can be improved. Model experiments show that the MTS-Bagging model suitable for financial distress prediction. The accuracy of the MTS-Bagging model outperforms the original Bagging ensemble prediction model. In addition, for the financial distress prediction, the risk cost of the two type errors is different. Considering the risk cost of misclassification, the error rate of MTS-Bagging model is lower than other modles in the research.
Thirdly, we research on the model for financial distress prediction based on sliding time window technology. Because of the data samples of financial distress prediction come from different years, there may be a concept drift problem in the modeling. The concept drift problem is solved by the sliding time window and Mahalanobis-Taguchi System, including feature dynamic selection and sample dynamic selection. The prediction model experiment is carried on the different width of the sliding time window. The results show that the concept drift of the financial data does exist. Select the appropriate width of the sliding time window can reduce the adverse effects of the drift concept.
Finally, we research on the model for financial distress prediction based on the incremental learning system. Given the lack of incremental learning ability of existing prediction model, an incremental learning system based on the classifier ensemble is designed. Financial category oriented knowledge is applied to realize the dynamic selection for classifier ensemble subsystem. Then the financial distress prediction model with incremental learning ability is build. Model experiments show that, the financial distress prediction model based on the incremental learning system is stable and adaptable. Incremental prediction model outperforms other models, which is an effective financial distress prediction method.
引文
[1]Altman E I. Financial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcy[J]. Journal of Finance,1968,23(4):589-609
[2]Deakin E B. A Discriminant Analysis of Prediction of Business Failure[J]. Journal of Accounting Research,2002:167-169
[3]Casey J R. Using Operating Cash Flow Data to Predict Financial Distress:Some Extensions[J]. Journal of Accounting Research,1985:140-149
[4]Julio P, Luis R, Chabela de la Torre. Estimating Financial Distress Likelihood[J]. Journal of Business Research,2008:995-1003
[5]Beaver W H. Financial Ratios as Predictors of Failure, Empirical Research in Accounting:Selected study[J]. Journal of Accounting Research,1966, (5):23-41
[6]Carmichael S H, Zopounidis C. A Survey of Business Failures with an Emphasis on Prediction Methods and Industrial Applications[J]. European Journal of Operational Research,1972, (29).487-513
[7]Scott J. The Probability of Bankruptcy:a Comparison of Empirical Predication and Theoretical Model [J]. Journal of Banking and Finance,1981, (12):317-344
[8]Paul P M, Jan B. Bankruptcy Prediction:the Influence of the Year Prior to Failure Selected for Model Building and the Effects in a Perid of Economic Decline[J]. Intelligent Systems in Accounting, Finance and Management,1992, (13):95-112
[9]Ross Neophytou, Andreas Charitou. Predicting Corporate Failure:Empirical Evidence for the UK[J]. Corporate Finance,2000, (9):457-471
[10]谷祺,刘淑莲.财务危机企业投资行为分析与对策[J].会计研究,1999,(10)：28-31
[11]孙星,邱菀华.企业财务危机预警双基点距离比值法[J].管理工程学报,2005,19(3)：106-110
[12]李秉祥.基于资本市场信息的上市公司财务危机动态预测模型研究[J1.管理工程学报,2005,19(4)：.49-52
[13]杨淑娥,黄礼.基于BP神经网络的上市公司财务预警模型[J].系统工程理论与实践,2005,(1)：12-19
[14]陈静.上市公司财务恶化预测的实证分析[J].会计研究,1999,(1)：31-38
[15]梁琪.企业经营管理预警：主成分分析在Logistic回归方法中的应用[J].管理工程学报,2005,19(1)：100-103
[16]谢纪刚,裘正定,韩彦俊.上市公司财务困境预测模型比较研究[J11.系统工程理论与实践,2005,(9)：29-35
[17]Fitzpatrick P J. A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firms[J]. Certified Public Accountant,1932, (2):589-605
[18]Platt H D, Marjorie B. Predicting Corporate Financial Distress:Reflections on Choice-based Sample Bias[J]. Journal of Economics and Finance,2002, (2):134-140
[19]张玲.财务危机预警分析判别模型[J].数量经济技术经济研究,2000,(3)：49-51
[20]高培业,张道奎.企业失败判别模型实证研究[J][.统计研究,2000,(10)：35-63
[21]陈晓,陈治鸿.中国上市公司的财务困境预测[J].中国会计与财务研究,2000,92(3)：55-72
[22]姜秀华.公司治理弱化与财务危机：一个预测模型[J].南开管理评论,2001(5)：54-60
[23]吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].经济研究,2001(6)：45-51
[24]周首华,杨济华,王平.论财务危机的预警分析——F分数模式[J].会计研究,1996,(8)：8-11
[25]Aziz A, Emanuel D, Lawson G. Bank Prediction:an Investigation of Cash Flow Based Models[J]. Journal of Management Studies,1988, (25):1-33
[26]姜守志,林淑辉.谈现金流量表的分析[J].山西财经大学学报,2000,(3)：87-90.
[27]梁飞媛.基于现金流的财务危机预警系统实证研究[J].贵州财经学院学报,2005,(4)：56-60
[28]董京燕,赵秀英.基于现金流量指标的中小企业财务预警研究[J].中国水电医学,2010,(2)：124-126
[29]章之旺.现金流量在财务困境预测中的信息含量实证研究——来自2003-2004年度ST公司的新证据[J].中国管理科学,2004,(6)：23-28
[30]吴春燕,干胜道.影响财务危机预警的现金增量指标研究[J].中国证券期货,2011,(9)：100
[31]Sanjai Bhagat, Brian Bolton. Corporate Governance and Firm Performance [J]. Journal of Corporate Finance,2008, (14):257-273
[32]Simpson W G, Gleason A E. Board Structure, Ownership, and Financial Distress in Banking Firms[J]. International Review of Economics and Finance,1999, (8):281-292
[33]Todd Mitton. A Cross-firm Analysis of the Impact of Corporate Governance on the East Asian Financial Crisis[J]. Journal of Financial Economics,2002,64:215-241
[34]Ohlson James. Financial Ratios and the Probabilistic Prediction of Bankruptcy [J]. Journal of Accounting Research,1980,18:109-131
[35]Gilson, Stuart C.Management Turnover and Financia Distress[J]. Journal of Financial Economics,1989,12:36-41
[36]Baek J S, Kang J K. Corporate Governance and Firm Value:Evidence from the Korean Financial Crisis[J]. Journal of Financial Econmics,2004,71:265-313
[37]张友棠.财务预警系统创新研究[D].华中科技大学博士学位论文,2002
[38]刘芳,陈浩.纳入EVA及非财务指标的信贷风险综合预警系统模型[J].价值工程,2009,(7)：155-158
[39]刘露,蒋志林.试析财务预警的新兴指标[J].财会月刊(综合),2006,(11)：38-39
[40]曹德芳,赵希男,王宇星.基于董事会治理因素的财务危机预警模型的构建[J].东北大学学报(自然科学版),2007,28(9)：1359-1353
[41]孙晓琳.基于Kalman滤波和BP神经网络的财务危机动态预警模型研究[D].哈尔滨工业大学博士学位论文,2010
[42]Zmijewski M E. Methodological Issues Related to the Estimation of Financial Distress Prediction Models[J]. Journal of Accounting Research,1984, (22):59-82
[43]Dambolena I G, Khoury S J. Ratio Stability and Corporate Failure[J]. Journal of Finance,1980, (35):1017-1026
[44]Laitimen E.K. Financial Predictors for Different Phases of the Failure Process[J]. Omega,1993, (21):215-228
[45]Martin D.Early Warning of Bank Failure[J]. Journal of Banking and Finance,1977, (1):249-276
[46]Ohlson J A. Financial Ratios and the Probabilistic Prediction of Bankruptcy [J]. Journal of Accounting Research,1980,(18):109-131
[47]Sjur Westgaard, Nico van der Wijst. Default Probabilities in a Corporate Bank Portfolio:A Logistic Model Approach[J], European Journal of Operational Research,2001, (12):338-349
[48]Erkki K L, Laitinen T. Bankruptcy Prediction:Application of the Taylor's Expansion in Logistic Regression[J]. International Review of Financial Analysis,2000, (9):327-349
[49]Zavgren C. The Prediction of Corporate Failure:the State of The Art[J]. Journal of Accounting Literature,2007, (8):434-440
[50]巢剑雄.企业财务困境预警模型[J].湖南大学学报(自然科学版),2005,32(1)：125-128
[51]宋鹏,张信东.基于Logistic模型的上市公司财务危机预警研究[J].经济问题,2009,(8)：50-52
[52]McClelland, Rumelhart. Parllel Distributed Processing[M]. Vols land 2, MIT Press, 1986
[53]Odom M, Sharda R. A Neural Networks Model for Bankruptcy Prediction[J]. Proceedings of the IEEE International Conference on Neural Network,1990:163-165
[54]Jain B A, Barin N N. Performance Evaluation of Neural Network Decision Models. Journal of Management Information Systems,1997, (14):201-216
[55]Arzum Erken Celik, Yalcin Karatepe. Evaluating and Forecasting Banking Crises through Neural Network Models:An Application for TURKISH Banking Sector[J]. Expert Systems with Applications,2007, (11):809-815
[56]Abir Jaafar Hussain, Adam Knowles, Paulo J G, Lisboa Wael E. Financial Time Series Prediction Using Polynomial Pipelined Neural Networks[J]. Expert Systems with Applications,2007, (8):1-32
[57]杨淑娥,王乐平.基于BP神经网络和面板数据的上市公司财务危机预警[J1.系统工程理论与实践,2007,(2)：61-67
[58]马超群,吴丽华.基于邻域粗糙集和神经网络的财务预警研究[J].软科学,2009,23(11)：123-125
[59]刘新允,庞清乐,刘爱国.基于遗传神经网络的财务危机预警研究[J].商业研究,2007,(9)：6-9
[60]Frydman H, Altman E I, Kao D..Introducing Recursive Partitioning for Financial Classification:the Case of Financial Distress[J]. Journal of Finance,1985,40(1):269-291
[61]Li H, Sun J, Wu J. Predicting Business Failure Using Classification and Regression Tree:An Empirical Comparison with Popular Classical Statistical Methods and Top Classification Mining Methods[J]. Expert Systems with Applications,2010, (37): 5895-5904
[62]Chen M Y. Predicting Corporate Financial Distress Based on Integration of Decision Tree Classification and Logistic Regression[J]. Expert Systems with Applications,2011, (38):11261-11272.
[63]姚靠华,蒋艳辉.基于决策树的财务预警[J].系统工程,2005,(10)：102-106
[64]Holland J H. Adaptation in Natural and Artificial Systems[M]. The University of Michigan Press,1975
[65]Varetto F. Genetic Algorithms Applications in the Analysis of Insolvency risk[J]. Journal of Banking and Finance,1998, (22):1421-1439
[66]Shin K S, Lee Y J A. Genetic Algorithm Application in Bankruptcy Prediction Modeling[J]. Expert Systems with Applications,2002, (23):321-328
[67]Ahn Hyunchui, Kim Kyoung-jae. Bankruptcy Prediction Modeling with Hybrid Case-reasoning and Genetic Algorithm Approach[J]. Applied Soft Computing,2009, (3): 599-607
[68]李清.基于遗传算法的上市公司财务危机预测模型研究[J].数理统计与管理,2008,27(5)：928-937
[69]宋新平,丁永生,曾月明.基于遗传算法的同步优化方法在财务困境预警中的应用[J].预测,2009,28(1)：48-55
[70]孙洁,李辉.遗传算法优化灰色案例推理的财务困境预测[J].科研管理,2009,30(2)：119-125
[71]Pawlak Z. Rough Sets[J]. International Journal of Information and Computer Sciences, 1982, (11):341-356
[72]Pawlak Z. Rough Set Theory and its Applications to Data Analysis[J]. Cybernetics and Systems,1998,29(1):661-688
[73]Dimitras A I, Slowinski R, Susmaga R et al. Business Failure Prediction Using Rough Sets[J]. European Journal of Operational Research,1999, (114):263-280
[74]Yao J T, Herbert J P. Financial Time-series Analysis with Rough Sets[J]. Applied Soft Computing,2009. (9):1000-1007
[75]马若微.基于粗糙集与信息熵的上市公司财务困境预警指标的确立[J].当代经济科学,2005,27(2)：45-50
[76]Cheol S P, Ingoo H. A Case-based Reasoning with the Feature Weights Derived by Analytic Process for Bankruptcy Prediction[J]. Expert System with Applications,2002, (23):255-264
[77]Jo H, Han I. Integration of Case-Based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction[J]. Expert Systems with Applications, 1996,11(4):415-422
[78]Park C S, Han I. A Case-Based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction[J]. Expert Systems with Applications,2002,23(3):255-264
[79]Hongkyu J, Han I, Lee H. Bankruptcy Prediction Using Case-based Reasoning, Neural Networks, and Discriminate Analysis[J]. Expert Systems with Applications,1997, (13):97-108
[80]Li H, Sun J. Majority Voting Combination of Multiple Case-based Reasoning for Financial Distress Prediction[J]. Expert System with Applications,2009,36(3):4363-4373
[81]黄继鸿,姚武,雷战波.基十案例推理的企业财务危机智能预警支持系统研究[J［. 系统工程理论与实践,2003,12：46-51
[82]Fan A, Palaniswami M. Selecting Bankruptcy Predictors Using a SupportVector Machine Approach[C]. Proceeding of the International Joint Conference on Neural Networks,2000, (6):354-359
[83]Shin K S, Lee T S, Kim H J. An Application of SupportVector Machines in Bankruptcy Prediction Model[J]. Expert Systems with Applications,2005, (28):127-135.
[84]Min J H, Lee Y C. Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters[J]. Expert Systems with Applications,2005, 28:128-134
[85]吴冬梅,朱俊,庄新田,杨霖.基于支持向量机的财务危机预警模型[J].东北大学学报(自然科学版),2010,31(4)：601-604
[86]Theodossiou P. Predicting Shifts in the Mean of a Multivariate Time Series Process: an Application in Predicting Business Failures[J]. Journal of the American Statistical Association,1993, (88):441-449
[87]谭久均.财务指标与违约距离相融合的上市公司财务预警模型[J].系统工程,2005,23(9)：112-117
[88]马若微.KMV模型运用于中国上市公司财务困境预警的实证检验[J].数理统计与管理,2006,25(5)：593-600
[89]佘元冠,刘建国.企业失败动态预测的ARMA-EWMA模型及其应用[J].系统工程,2006,24(12)：84-89.
[90]陈磊,任若恩,曹汉平.公司多阶段财务危机动态预警研究[J].系统工程理论与实践,2008,(11)：29-35
[91]孙晓琳,田也壮,王文彬.基于Kalman滤波的企业财务危机动态预警模型[J].系统管理学报,2010,19(4)：408-414
[92]吴芃,仲伟俊,吴应宇.基于修正Jones盈余管理模型的财务危机预警研究[J].商业经济与管理,2010,(8)：67-73
[93]江一涛,张顺明.基于Cox模型研究财务困境的行业差异性[J].数理统计与管理,2011,30(2)：339-345
[94]Back B, Laitinen T, Sere K. Neural Network and Genetic Algorithm for Bankruptcy Prediction [J]. Expert Systems with Applications,1996,11 (4):407-413
[95]Wallrafen J, Protzel P, Popp H. Genetically Optimized Neural Network Classifiers for Bankruptcy Prediction[C]. Proceedings of the 29 the Annual Hawaii International Conference on System Sciences,1996, (3):65-69
[96]彭静,彭勇,欧阳令南.基于粒子算法和支持向量机的财务危机预警模型[J].上 海交通大学学报,2008,42(4)：615-620
[97]岑涌,钟萍,罗林开.基于GA-SVM的企业财务困境预测[J].计算机工程,2008,34(7)：223-225
[98]李晓峰,徐玖平.企业财务危机预警Rough-ANN模型的建立及其应用[J].系统工程理论与实践,2004,(10)：8-14
[99]Sun J, Li H. Listed Companies'Financial Distress Prediction Based on Weighted Majority Voting Combination of Multiple Classifiers [J]. Expert System with Applications, 2008,35(3):818-827
[100]刘旻,罗慧.上市公司财务危机预警分析——基于数据挖掘的研究[J].数理统计与管理,2004,23(3)：51-56
[101]McKee T E, Lensberg T. Genetic Programming and Rough Sets:A Hybrid Approach to Bankruptcy Classification[J]. European Journal of Operational Research,2002, (138): 436-451
[102]Tung W L, Quek C, Cheng P. GenSo-EWS:A Novel Neural-fuzzy Based Early Warning System for Predicting Bank Failures[J]. Neural Networks,2004, (17):567-587
[103]Bian H, Mazlack L. Fuzzy-rough Nearest Neighbour Classification Approach[C]. Proceeding of 22nd International Conference of the North American Fuzzy Information Processing Society, (NAFIPS 2003) Chicago,2003:500-505
[104]Cho S, Kim J. Bae J K. An Integrative Model with Subject Weight Based on Neural Network Learning for Bankruptcy Prediction[J]. Expert System with Applications,2009, 36(1):403-410
[105]Lin F Y, McClean S. A Datamining Approach to the Prediction of Corporate Failure[J]. Knowledge Based Systems,2001 (14):189-195
[106]Ahn B A, Cho S S, Kim C Y. The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction[J]. Expert Systems with Applications,2000, (18):65-74
[107]韩建光,惠晓峰,孙洁.基于多特征子集组合分类器的企业财务困境预测[J].系统管理学报,2010,19(4)：420-427
[108]West D, Dellana S, Qian J. Neural Network Ensemble Strategies for Financial Decision Applications[J]. Computers & Operations Research,2005, (32):2543-2559
[109]Tsai C F, Wu J W. Using Neural Network Ensembles for Bankruptcy Prediction and Credit Scoring[J]. Expert Systems with Applications,2008,34(4):2639-2649
[110]Nanni L, Lumini A. An Experimental Comparison of Ensemble of Classifiers for Bankruptcy Prediction and Credit Scoring[J]. Expert Systems with Applications,2009, 36(2):3028-3033
[111]Kim M J, Kang D K. Ensemble with Neural Networks for Bankruptcy Prediction[J]. Expert Systems with Applications,2010,37(4):3373-3379
[112]Hung C, Chen J H. A Selective Ensemble Based on Expected Probabilities for Bankruptcy Prediction[J]. Expert Systems with Applications,2010,36(3):5297-5303
[113]Sun J, Jia M Y, Li H. AdaBoost Ensemble for Financial Distress Prediction:An Empirical Comparison with Data from Chinese Listed Companies [J]. Expert Systems with Applications,2011,38:9305-9312
[114]谢纪刚,裘正定,周铖,王静.分类器集成在财务危机预测中的应用研究[J].复旦学报(自然科学版),2004,43(5)：785-788
[115]李秉祥.基于模糊神经网络的企业财务危机非线性组合预测方法应用[J].管理工程学报,2005,19(])：19-23
[116]韩建光,惠晓峰,孙洁.遗传算法选择性集成多分类器的企业财务困境预测[J].系统工程,2010,28(8)：9-15
[117]Kumar Ravi P, Ravi V. Bankruptcy Prediction in Banks and Firms Via Statistical and Intelligent Techniques-A Review[J]. European Journal of Operational Research,2007, (180):1-8
[118]刑精平.企业财务危机预警分析[D].西南财经大学博士学位论文,2003
[119]殷彦辉.财务危机预警系统与企业发展辨析[J].现代审计与会计.2006,(2)：20
[120]Lindsay D H, Campbell A. A Chaos Approach to Bankruptcy Prediction[J]. Journal of Applied Business Research,1996,12(4):1-9
[121]Ho S Y, Saunders A. The Determinants of Bank Interest Margins[J]. Journal of Financial and Quantitative Analysis,1980,15(1):191-200
[122]Scott J. The Probability of Bankruptcy:A Comparison of Empirical Predictions and Theoretical Models[J]. Journal of Banking and Finance,1981,5(3):317-344
[123]佘丛国,席酉民.我国企业预警研究理论综述[J].预测,2003,22(2)：23-29
[124]刘广利.基于支持向量机的经济预警方法研究[D].中国农业大学博士学位论文,2003：8-16
[125]Bates J M, Granger C W J. Combination of Forecasts[J]. Operations Research Quarterly,1969,20(4):451-468
[126]黄曦,邹安全.基于BP神经网络的企业财务危机组合预警研究[J].经济与管理研究,2009,(5)：87-91
[127]何跃,何正林,周欣胤.基于数据挖掘的固网大客户流失组合预警[J].软科学,2012,26(1)：128-131
[128]Dietterich T. Machine Learning Research:Four Current Directions[J]. Artificial Intelligence,1998,18(4):97-136
[129]Dietterieh T. Ensemble Methods in Machine Learning[C]. Proceeding of MCS, Italy, LNCS, Springer,2000:1-15
[130]Hansen L K, Salamon P. Neural Network Ensembles[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001
[131]Sollich P, Krogh A. Learning with ensembles:How overfitting can be useful, in: D.Touretzky,M.Mozer,M.Hasselmo(Eds.), Advances in Neural Information Processing Systems,vol.8, MIT Press, Cambridge, MA,1996:190-196
[132]Kittler J, Hatef M, Duin W, et al. On Combining Classifiers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(3):226-239
[133]Valiant L G. A theory of learnability[J]. Communication ACM,1984,27:1134-1142.
[134]Schapire R E. The strength of weak learnability[J]. Machine Learning,1990, (2): 197-227
[135]Perrone M P, Cooper L N. When Networks Disagree:Ensemble Method for Neural Networks. In:Mammone R J ed. Artificial Neural Networks for Speech and Vision, London:Chapman-Hall,1993:126-142
[136]Breiman L. Bagging Predictors [J]. Machine Learning,1996, (2):123-140
[137]Zhou Z H, Wu J X, Tang W. Ensembling Neural Networks:Many Could Be Better Than All[J]. Artificial Intelligence,2002, (137):239-263
[138]Woods K, Kegelmeyer W P, Bowyer K. Combination of Multiple Classifiers Using Local Accuracy Estimates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(4):405-410
[139]Ho T K. The Random Subspace Method for Constructing Decision Forests [J]. IEEE Transactions on pattern Analysis and Machine Intelligence,1998, (8):832-844
[140]Dietterich T G. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:Bagging, Boosting, and Randomization[J]. Machine Learning,2000,40:139-157
[141]Dash M, Liu H. Consistency Based Search in Feature Selection[J]. Artificial Intelligence,2003,151:155-176
[142]Shimshoni Y, Intrator N. Classification of Seismic Signals by Integrating Ensembles of Nemal Networks[J]. IEEE Transactions on Signal Processing,1998,46(5):1194-1201
[143]Zhang X, Mesirov J, Waltz D. Hybrid System for Protein Secondary Structure Prediction[J]. Journal of Molecular Biology,1992,225:1049-1063
[144]Sharkey A J C,Sharkey N E, Cross S S. Adapting an Ensemble Approach for the Diagnosis of Breast Cancer[C]. Proceedings of International Conference on Artificial Neural Networks, Skovde, Sweden,1998:281-286
[145]Schenker A, Bunke H, Last M, Kandel A. Building Graph-based Classifier Ensembles by Random Node Selection[J]. Roli F. Kittler J, Windeatt T.(Eds.)MCS LNCS, 2004:214-222
[146]Krogh A, Vedelsby J. Neural Network Ensembles, Cross Validation, and Active Learning[J]. in:Touretzky D, Leen T. Advances in Neural Information Processing Systems,vol.7,MIT Press, Cambridge, MA,1995:231-238
[147]Partridge D. Network Generalization Differences Quantified [J]. Neural Networks, 1996, (9):263-271
[148]Partridge D, Yates W B. Engineering Multiversion Neural-net Systems[J]. Neural Computation,1996, (8):869-893
[149]Shipp C A, Kuncheva L I. Relationships Between Combination Methods and Measures of Diversity in Combining Classifiers[J]. Information Fusion,2002, (3):135-148
[150]Banfield R E, Hall L O, Bowyer K W, Kegelmeyer W P. Ensemble Diversity Measures and Their Application to Thinning[J]. Information Fusion.2005. (6):49-62
[151]Ruta D, Gabrys B. Classifier Selection for Majority Voting[J]. Information Fusion, 2005, (6):63-81
[152]Tsymbal A, Pechenizkiy M, Cunningham P. Diversity in Search Strategies for Ensemble Feature Selection[J]. Information Fusion,2005, (6):83-98
[153]Opitz D, Maclin R. Popular Ensemble Methods:An empirical study [J]. Journal of Artificial Intelligence Research,1999,11:169-198
[154]庄进发,罗键,彭彦卿,黄春庆,吴长庆.基于改进随机森林的故障诊断方法研究[J].计算机集成制造系统,2009,15(4)：777-785
[155]Breiman L. Random Forests[J]. Machine Learning,2001,45(1):5-32
[156]Lippmann R P. An Introduction to Computing with Neural Nets[J]. IEEE ASSP Magazine,1987, (4):4-22
[157]Cyberko G. Approximations by Superpositions of a Sigmoidal Function[M]. Math Control Signal System,1989
[158]Hema Rao, Alexey G. Ivakhnenko, Inductive Leaning Algorithms for Complex System Modeling[M]. CRC Press, Inc,1994
[159]Kuncheva L, Whitaker C. Measures of Diversity in Classifier Ensembles and Their Relationship with Ensemble Accuracy [J]. Machine Learning,2003,51(2):181-207
[160]Gunter S.and Bunkfe H. Feature Selection Algorithms for the Generation of Multiple Classifier Systems and Their Application to Handwritten Word Recognition[J]. Pattern Recognition Letters,2004, (25):1323-1336
[161]Maimon O, Rokach L. Improving Supervised Learning by Feature Decomposition. Eiter T, Schewe K D. (Eds.) FoIKS, LNCS 2284,2002:178-196
[162]Last M, Bunke H, Kandel A. A Feature-based Serial Approach to Classifier Combination[J]. Pattern Analysis and Applications,2002,5:385-398
[163]Taguchi G, Jugulum R. New Trends in Multivariate Diagnosis[J]. Sankhya:The Indian Journal of Statistics,2000, (62):233-248
[164]Jugulum R, Taguchi G. Direction of Abnormals and Treatment of Noise Factors in Multidimensional Systems:A Discussion[J]. Journal of Quality Engineering Forurn,2001, (3):37-44
[165]郑称德,韩之俊.MTS设计原理及其设计模型[J].管理工程学报,2000,(3)：43-46
[166]Mahalakshmi P, Ganesan K. Mahalanobis Taguchi System Based Criteria Selection for Shrimpaquaculture Development[J]. Computers and Electronics in Agriculture,2009, 65:192-197
[167]Huang C L, Hsu T S, Liu C M. The Mahalanobis-Taguchi System-Neural Network Algorithm for Data-mining in Dynamic Environments[J]. Expert Systems with Applications.2009,36:5475-5480
[168]Lee Yu-Cheng, Teng Hsiao-Lin. Predicting the Financial Crisis by Mahalanobis-Taguchi System-Examples of Taiwan's Electronic Sector[J]. Expert Systems with Applications,2009,36:7469-7478
[169]薜跃,韩之俊,盛党红.MTS法用于上市公司财务质量评估初探[J].数理统计与管理,2005,24(1)：81-85
[170]宗鹏,曾凤章.基于MTS的企业可持续发展评价体系研究[J].科学技术与工程,2006,6(8)：1163-1166
[171]王洪祥,韩之俊,李晓萍.基于MTS的县级市城市化评价[J].城市发展研究,2009,16(7)：33-38
[172]李国正,李丹.集成学习中特征选择技术[J］.上海大学学报(自然科学版),2007,(10)：598-604
[173]凌锦江,陈兆乾,周志华.基于特征选择的神经网络集成方法[J].复旦学报(自然科学版),2004,(10)：685-688
[174]张东波,黄辉先,王耀南.基于粗糙集约简的特征选择神经网络集成技术[J]. 控制与决策,2010,(3)：371-377
[175]Bryll R, Gutierrez O R, Quek F. Attribute Bagging:Improving Accuracy of Classifier Ensembles by Using Random Feature Subset[J]. Pattern Recognition,2003,36(3): 1291-1302
[176]Widmer G, Kubat M. Learning in the Presence of Concept Drift and Hidden Contexts[J]. Machine Learning,1996,23(1):69-101
[177]柴玉梅,周驰,王黎明.数据流上概念漂移的检测和分类[J].小型微型计算机系统,2011,32(3)：421-425
[178]Gao J, Ding B, Fan W, Han J, Yu P S. Classifying Data Streams with Skewed Class Distributions and Concept Drifts[J]. IEEE Internet Computing,2008,12(6):37-49
[179]Widmer G, Kubat M. Effective Learning in Dynamic Environments by Explicit Context Tracking[C]. Proceeding of the 6th European Conference on Machine Learning, LNCS667,1993:227-243
[180]钟玉峰,雷国华.一种基于滑动窗口技术的入侵检测方法[J].信息技术,2009,7：166-170
[181]Delanya S J, Cunninghamb P, Tsymbalb A, Coyle L. A Case-based Technique for Tracking Concept Drift in Spam Filtering[J]. Knowledge-Based Systems,2005, (18): 187-195
[182]孙洁,李辉,韩建光.基于滚动时间窗口支持向量机的财务困境预测动态建模[J].管理工程学报,2010,24(4)：174-180
[183]Sun J, Li H. Dynamic Financial Distress Prediction Using Instance Selection for the Disposal of Concept Drift[J]. Expert Systems with Applications,2011, (38):2566-2576
[184]Sun J, He K Y, Li H. SFFS-PC-NN Optimized by Genetic Algorithm for Dynamic Prediction of Financial Distress with Longitudinal Data Streams[J]. Knowledge-Based Systems,2001, (3):1-11
[185]Polikar R, Udpa L, Udpa S S, et al. Learn++:An Incremental Learning Algorithm for Supervised Neural Networks[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part C:Applications and Reviews,2001, (4):497-500
[186]Schlimmer J C, Granger R H. Incremental Learning from Noisy Data[J]. Machine Learning,1986, (1):317-354
[187]Street W N, Kim Y S. A Streaming Ensemble Algorithm for Large-scale Classification[C]. Proceeding of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2001:377-382
[188]Wang H, Fan W, Yu P S, Han J. Mining Concept-drifting Data Streams Using Ensemble Classifiers[C]. Proceeding of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2003, (8):24-27
[189]Sun J, Li Hui, Zhang Meng. Multiple Classifiers Hybrid Combination For Companies' Financial Distress Prediction[J]. Systems Engineering Theory and Practice, 2009,29(2):78-86
[2]Deakin E B. A Discriminant Analysis of Prediction of Business Failure[J]. Journal of Accounting Research,2002:167-169
[3]Casey J R. Using Operating Cash Flow Data to Predict Financial Distress:Some Extensions[J]. Journal of Accounting Research,1985:140-149
[4]Julio P, Luis R, Chabela de la Torre. Estimating Financial Distress Likelihood[J]. Journal of Business Research,2008:995-1003
[5]Beaver W H. Financial Ratios as Predictors of Failure, Empirical Research in Accounting:Selected study[J]. Journal of Accounting Research,1966, (5):23-41
[6]Carmichael S H, Zopounidis C. A Survey of Business Failures with an Emphasis on Prediction Methods and Industrial Applications[J]. European Journal of Operational Research,1972, (29).487-513
[7]Scott J. The Probability of Bankruptcy:a Comparison of Empirical Predication and Theoretical Model [J]. Journal of Banking and Finance,1981, (12):317-344
[8]Paul P M, Jan B. Bankruptcy Prediction:the Influence of the Year Prior to Failure Selected for Model Building and the Effects in a Perid of Economic Decline[J]. Intelligent Systems in Accounting, Finance and Management,1992, (13):95-112
[9]Ross Neophytou, Andreas Charitou. Predicting Corporate Failure:Empirical Evidence for the UK[J]. Corporate Finance,2000, (9):457-471
[10]谷祺,刘淑莲.财务危机企业投资行为分析与对策[J].会计研究,1999,(10)：28-31
[11]孙星,邱菀华.企业财务危机预警双基点距离比值法[J].管理工程学报,2005,19(3)：106-110
[12]李秉祥.基于资本市场信息的上市公司财务危机动态预测模型研究[J1.管理工程学报,2005,19(4)：.49-52
[13]杨淑娥,黄礼.基于BP神经网络的上市公司财务预警模型[J].系统工程理论与实践,2005,(1)：12-19
[14]陈静.上市公司财务恶化预测的实证分析[J].会计研究,1999,(1)：31-38
[15]梁琪.企业经营管理预警：主成分分析在Logistic回归方法中的应用[J].管理工程学报,2005,19(1)：100-103
[16]谢纪刚,裘正定,韩彦俊.上市公司财务困境预测模型比较研究[J11.系统工程理论与实践,2005,(9)：29-35
[17]Fitzpatrick P J. A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firms[J]. Certified Public Accountant,1932, (2):589-605
[18]Platt H D, Marjorie B. Predicting Corporate Financial Distress:Reflections on Choice-based Sample Bias[J]. Journal of Economics and Finance,2002, (2):134-140
[19]张玲.财务危机预警分析判别模型[J].数量经济技术经济研究,2000,(3)：49-51
[20]高培业,张道奎.企业失败判别模型实证研究[J][.统计研究,2000,(10)：35-63
[21]陈晓,陈治鸿.中国上市公司的财务困境预测[J].中国会计与财务研究,2000,92(3)：55-72
[22]姜秀华.公司治理弱化与财务危机：一个预测模型[J].南开管理评论,2001(5)：54-60
[23]吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].经济研究,2001(6)：45-51
[24]周首华,杨济华,王平.论财务危机的预警分析——F分数模式[J].会计研究,1996,(8)：8-11
[25]Aziz A, Emanuel D, Lawson G. Bank Prediction:an Investigation of Cash Flow Based Models[J]. Journal of Management Studies,1988, (25):1-33
[26]姜守志,林淑辉.谈现金流量表的分析[J].山西财经大学学报,2000,(3)：87-90.
[27]梁飞媛.基于现金流的财务危机预警系统实证研究[J].贵州财经学院学报,2005,(4)：56-60
[28]董京燕,赵秀英.基于现金流量指标的中小企业财务预警研究[J].中国水电医学,2010,(2)：124-126
[29]章之旺.现金流量在财务困境预测中的信息含量实证研究——来自2003-2004年度ST公司的新证据[J].中国管理科学,2004,(6)：23-28
[30]吴春燕,干胜道.影响财务危机预警的现金增量指标研究[J].中国证券期货,2011,(9)：100
[31]Sanjai Bhagat, Brian Bolton. Corporate Governance and Firm Performance [J]. Journal of Corporate Finance,2008, (14):257-273
[32]Simpson W G, Gleason A E. Board Structure, Ownership, and Financial Distress in Banking Firms[J]. International Review of Economics and Finance,1999, (8):281-292
[33]Todd Mitton. A Cross-firm Analysis of the Impact of Corporate Governance on the East Asian Financial Crisis[J]. Journal of Financial Economics,2002,64:215-241
[34]Ohlson James. Financial Ratios and the Probabilistic Prediction of Bankruptcy [J]. Journal of Accounting Research,1980,18:109-131
[35]Gilson, Stuart C.Management Turnover and Financia Distress[J]. Journal of Financial Economics,1989,12:36-41
[36]Baek J S, Kang J K. Corporate Governance and Firm Value:Evidence from the Korean Financial Crisis[J]. Journal of Financial Econmics,2004,71:265-313
[37]张友棠.财务预警系统创新研究[D].华中科技大学博士学位论文,2002
[38]刘芳,陈浩.纳入EVA及非财务指标的信贷风险综合预警系统模型[J].价值工程,2009,(7)：155-158
[39]刘露,蒋志林.试析财务预警的新兴指标[J].财会月刊(综合),2006,(11)：38-39
[40]曹德芳,赵希男,王宇星.基于董事会治理因素的财务危机预警模型的构建[J].东北大学学报(自然科学版),2007,28(9)：1359-1353
[41]孙晓琳.基于Kalman滤波和BP神经网络的财务危机动态预警模型研究[D].哈尔滨工业大学博士学位论文,2010
[42]Zmijewski M E. Methodological Issues Related to the Estimation of Financial Distress Prediction Models[J]. Journal of Accounting Research,1984, (22):59-82
[43]Dambolena I G, Khoury S J. Ratio Stability and Corporate Failure[J]. Journal of Finance,1980, (35):1017-1026
[44]Laitimen E.K. Financial Predictors for Different Phases of the Failure Process[J]. Omega,1993, (21):215-228
[45]Martin D.Early Warning of Bank Failure[J]. Journal of Banking and Finance,1977, (1):249-276
[46]Ohlson J A. Financial Ratios and the Probabilistic Prediction of Bankruptcy [J]. Journal of Accounting Research,1980,(18):109-131
[47]Sjur Westgaard, Nico van der Wijst. Default Probabilities in a Corporate Bank Portfolio:A Logistic Model Approach[J], European Journal of Operational Research,2001, (12):338-349
[48]Erkki K L, Laitinen T. Bankruptcy Prediction:Application of the Taylor's Expansion in Logistic Regression[J]. International Review of Financial Analysis,2000, (9):327-349
[49]Zavgren C. The Prediction of Corporate Failure:the State of The Art[J]. Journal of Accounting Literature,2007, (8):434-440
[50]巢剑雄.企业财务困境预警模型[J].湖南大学学报(自然科学版),2005,32(1)：125-128
[51]宋鹏,张信东.基于Logistic模型的上市公司财务危机预警研究[J].经济问题,2009,(8)：50-52
[52]McClelland, Rumelhart. Parllel Distributed Processing[M]. Vols land 2, MIT Press, 1986
[53]Odom M, Sharda R. A Neural Networks Model for Bankruptcy Prediction[J]. Proceedings of the IEEE International Conference on Neural Network,1990:163-165
[54]Jain B A, Barin N N. Performance Evaluation of Neural Network Decision Models. Journal of Management Information Systems,1997, (14):201-216
[55]Arzum Erken Celik, Yalcin Karatepe. Evaluating and Forecasting Banking Crises through Neural Network Models:An Application for TURKISH Banking Sector[J]. Expert Systems with Applications,2007, (11):809-815
[56]Abir Jaafar Hussain, Adam Knowles, Paulo J G, Lisboa Wael E. Financial Time Series Prediction Using Polynomial Pipelined Neural Networks[J]. Expert Systems with Applications,2007, (8):1-32
[57]杨淑娥,王乐平.基于BP神经网络和面板数据的上市公司财务危机预警[J1.系统工程理论与实践,2007,(2)：61-67
[58]马超群,吴丽华.基于邻域粗糙集和神经网络的财务预警研究[J].软科学,2009,23(11)：123-125
[59]刘新允,庞清乐,刘爱国.基于遗传神经网络的财务危机预警研究[J].商业研究,2007,(9)：6-9
[60]Frydman H, Altman E I, Kao D..Introducing Recursive Partitioning for Financial Classification:the Case of Financial Distress[J]. Journal of Finance,1985,40(1):269-291
[61]Li H, Sun J, Wu J. Predicting Business Failure Using Classification and Regression Tree:An Empirical Comparison with Popular Classical Statistical Methods and Top Classification Mining Methods[J]. Expert Systems with Applications,2010, (37): 5895-5904
[62]Chen M Y. Predicting Corporate Financial Distress Based on Integration of Decision Tree Classification and Logistic Regression[J]. Expert Systems with Applications,2011, (38):11261-11272.
[63]姚靠华,蒋艳辉.基于决策树的财务预警[J].系统工程,2005,(10)：102-106
[64]Holland J H. Adaptation in Natural and Artificial Systems[M]. The University of Michigan Press,1975
[65]Varetto F. Genetic Algorithms Applications in the Analysis of Insolvency risk[J]. Journal of Banking and Finance,1998, (22):1421-1439
[66]Shin K S, Lee Y J A. Genetic Algorithm Application in Bankruptcy Prediction Modeling[J]. Expert Systems with Applications,2002, (23):321-328
[67]Ahn Hyunchui, Kim Kyoung-jae. Bankruptcy Prediction Modeling with Hybrid Case-reasoning and Genetic Algorithm Approach[J]. Applied Soft Computing,2009, (3): 599-607
[68]李清.基于遗传算法的上市公司财务危机预测模型研究[J].数理统计与管理,2008,27(5)：928-937
[69]宋新平,丁永生,曾月明.基于遗传算法的同步优化方法在财务困境预警中的应用[J].预测,2009,28(1)：48-55
[70]孙洁,李辉.遗传算法优化灰色案例推理的财务困境预测[J].科研管理,2009,30(2)：119-125
[71]Pawlak Z. Rough Sets[J]. International Journal of Information and Computer Sciences, 1982, (11):341-356
[72]Pawlak Z. Rough Set Theory and its Applications to Data Analysis[J]. Cybernetics and Systems,1998,29(1):661-688
[73]Dimitras A I, Slowinski R, Susmaga R et al. Business Failure Prediction Using Rough Sets[J]. European Journal of Operational Research,1999, (114):263-280
[74]Yao J T, Herbert J P. Financial Time-series Analysis with Rough Sets[J]. Applied Soft Computing,2009. (9):1000-1007
[75]马若微.基于粗糙集与信息熵的上市公司财务困境预警指标的确立[J].当代经济科学,2005,27(2)：45-50
[76]Cheol S P, Ingoo H. A Case-based Reasoning with the Feature Weights Derived by Analytic Process for Bankruptcy Prediction[J]. Expert System with Applications,2002, (23):255-264
[77]Jo H, Han I. Integration of Case-Based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction[J]. Expert Systems with Applications, 1996,11(4):415-422
[78]Park C S, Han I. A Case-Based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction[J]. Expert Systems with Applications,2002,23(3):255-264
[79]Hongkyu J, Han I, Lee H. Bankruptcy Prediction Using Case-based Reasoning, Neural Networks, and Discriminate Analysis[J]. Expert Systems with Applications,1997, (13):97-108
[80]Li H, Sun J. Majority Voting Combination of Multiple Case-based Reasoning for Financial Distress Prediction[J]. Expert System with Applications,2009,36(3):4363-4373
[81]黄继鸿,姚武,雷战波.基十案例推理的企业财务危机智能预警支持系统研究[J［. 系统工程理论与实践,2003,12：46-51
[82]Fan A, Palaniswami M. Selecting Bankruptcy Predictors Using a SupportVector Machine Approach[C]. Proceeding of the International Joint Conference on Neural Networks,2000, (6):354-359
[83]Shin K S, Lee T S, Kim H J. An Application of SupportVector Machines in Bankruptcy Prediction Model[J]. Expert Systems with Applications,2005, (28):127-135.
[84]Min J H, Lee Y C. Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters[J]. Expert Systems with Applications,2005, 28:128-134
[85]吴冬梅,朱俊,庄新田,杨霖.基于支持向量机的财务危机预警模型[J].东北大学学报(自然科学版),2010,31(4)：601-604
[86]Theodossiou P. Predicting Shifts in the Mean of a Multivariate Time Series Process: an Application in Predicting Business Failures[J]. Journal of the American Statistical Association,1993, (88):441-449
[87]谭久均.财务指标与违约距离相融合的上市公司财务预警模型[J].系统工程,2005,23(9)：112-117
[88]马若微.KMV模型运用于中国上市公司财务困境预警的实证检验[J].数理统计与管理,2006,25(5)：593-600
[89]佘元冠,刘建国.企业失败动态预测的ARMA-EWMA模型及其应用[J].系统工程,2006,24(12)：84-89.
[90]陈磊,任若恩,曹汉平.公司多阶段财务危机动态预警研究[J].系统工程理论与实践,2008,(11)：29-35
[91]孙晓琳,田也壮,王文彬.基于Kalman滤波的企业财务危机动态预警模型[J].系统管理学报,2010,19(4)：408-414
[92]吴芃,仲伟俊,吴应宇.基于修正Jones盈余管理模型的财务危机预警研究[J].商业经济与管理,2010,(8)：67-73
[93]江一涛,张顺明.基于Cox模型研究财务困境的行业差异性[J].数理统计与管理,2011,30(2)：339-345
[94]Back B, Laitinen T, Sere K. Neural Network and Genetic Algorithm for Bankruptcy Prediction [J]. Expert Systems with Applications,1996,11 (4):407-413
[95]Wallrafen J, Protzel P, Popp H. Genetically Optimized Neural Network Classifiers for Bankruptcy Prediction[C]. Proceedings of the 29 the Annual Hawaii International Conference on System Sciences,1996, (3):65-69
[96]彭静,彭勇,欧阳令南.基于粒子算法和支持向量机的财务危机预警模型[J].上 海交通大学学报,2008,42(4)：615-620
[97]岑涌,钟萍,罗林开.基于GA-SVM的企业财务困境预测[J].计算机工程,2008,34(7)：223-225
[98]李晓峰,徐玖平.企业财务危机预警Rough-ANN模型的建立及其应用[J].系统工程理论与实践,2004,(10)：8-14
[99]Sun J, Li H. Listed Companies'Financial Distress Prediction Based on Weighted Majority Voting Combination of Multiple Classifiers [J]. Expert System with Applications, 2008,35(3):818-827
[100]刘旻,罗慧.上市公司财务危机预警分析——基于数据挖掘的研究[J].数理统计与管理,2004,23(3)：51-56
[101]McKee T E, Lensberg T. Genetic Programming and Rough Sets:A Hybrid Approach to Bankruptcy Classification[J]. European Journal of Operational Research,2002, (138): 436-451
[102]Tung W L, Quek C, Cheng P. GenSo-EWS:A Novel Neural-fuzzy Based Early Warning System for Predicting Bank Failures[J]. Neural Networks,2004, (17):567-587
[103]Bian H, Mazlack L. Fuzzy-rough Nearest Neighbour Classification Approach[C]. Proceeding of 22nd International Conference of the North American Fuzzy Information Processing Society, (NAFIPS 2003) Chicago,2003:500-505
[104]Cho S, Kim J. Bae J K. An Integrative Model with Subject Weight Based on Neural Network Learning for Bankruptcy Prediction[J]. Expert System with Applications,2009, 36(1):403-410
[105]Lin F Y, McClean S. A Datamining Approach to the Prediction of Corporate Failure[J]. Knowledge Based Systems,2001 (14):189-195
[106]Ahn B A, Cho S S, Kim C Y. The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction[J]. Expert Systems with Applications,2000, (18):65-74
[107]韩建光,惠晓峰,孙洁.基于多特征子集组合分类器的企业财务困境预测[J].系统管理学报,2010,19(4)：420-427
[108]West D, Dellana S, Qian J. Neural Network Ensemble Strategies for Financial Decision Applications[J]. Computers & Operations Research,2005, (32):2543-2559
[109]Tsai C F, Wu J W. Using Neural Network Ensembles for Bankruptcy Prediction and Credit Scoring[J]. Expert Systems with Applications,2008,34(4):2639-2649
[110]Nanni L, Lumini A. An Experimental Comparison of Ensemble of Classifiers for Bankruptcy Prediction and Credit Scoring[J]. Expert Systems with Applications,2009, 36(2):3028-3033
[111]Kim M J, Kang D K. Ensemble with Neural Networks for Bankruptcy Prediction[J]. Expert Systems with Applications,2010,37(4):3373-3379
[112]Hung C, Chen J H. A Selective Ensemble Based on Expected Probabilities for Bankruptcy Prediction[J]. Expert Systems with Applications,2010,36(3):5297-5303
[113]Sun J, Jia M Y, Li H. AdaBoost Ensemble for Financial Distress Prediction:An Empirical Comparison with Data from Chinese Listed Companies [J]. Expert Systems with Applications,2011,38:9305-9312
[114]谢纪刚,裘正定,周铖,王静.分类器集成在财务危机预测中的应用研究[J].复旦学报(自然科学版),2004,43(5)：785-788
[115]李秉祥.基于模糊神经网络的企业财务危机非线性组合预测方法应用[J].管理工程学报,2005,19(])：19-23
[116]韩建光,惠晓峰,孙洁.遗传算法选择性集成多分类器的企业财务困境预测[J].系统工程,2010,28(8)：9-15
[117]Kumar Ravi P, Ravi V. Bankruptcy Prediction in Banks and Firms Via Statistical and Intelligent Techniques-A Review[J]. European Journal of Operational Research,2007, (180):1-8
[118]刑精平.企业财务危机预警分析[D].西南财经大学博士学位论文,2003
[119]殷彦辉.财务危机预警系统与企业发展辨析[J].现代审计与会计.2006,(2)：20
[120]Lindsay D H, Campbell A. A Chaos Approach to Bankruptcy Prediction[J]. Journal of Applied Business Research,1996,12(4):1-9
[121]Ho S Y, Saunders A. The Determinants of Bank Interest Margins[J]. Journal of Financial and Quantitative Analysis,1980,15(1):191-200
[122]Scott J. The Probability of Bankruptcy:A Comparison of Empirical Predictions and Theoretical Models[J]. Journal of Banking and Finance,1981,5(3):317-344
[123]佘丛国,席酉民.我国企业预警研究理论综述[J].预测,2003,22(2)：23-29
[124]刘广利.基于支持向量机的经济预警方法研究[D].中国农业大学博士学位论文,2003：8-16
[125]Bates J M, Granger C W J. Combination of Forecasts[J]. Operations Research Quarterly,1969,20(4):451-468
[126]黄曦,邹安全.基于BP神经网络的企业财务危机组合预警研究[J].经济与管理研究,2009,(5)：87-91
[127]何跃,何正林,周欣胤.基于数据挖掘的固网大客户流失组合预警[J].软科学,2012,26(1)：128-131
[128]Dietterich T. Machine Learning Research:Four Current Directions[J]. Artificial Intelligence,1998,18(4):97-136
[129]Dietterieh T. Ensemble Methods in Machine Learning[C]. Proceeding of MCS, Italy, LNCS, Springer,2000:1-15
[130]Hansen L K, Salamon P. Neural Network Ensembles[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001
[131]Sollich P, Krogh A. Learning with ensembles:How overfitting can be useful, in: D.Touretzky,M.Mozer,M.Hasselmo(Eds.), Advances in Neural Information Processing Systems,vol.8, MIT Press, Cambridge, MA,1996:190-196
[132]Kittler J, Hatef M, Duin W, et al. On Combining Classifiers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(3):226-239
[133]Valiant L G. A theory of learnability[J]. Communication ACM,1984,27:1134-1142.
[134]Schapire R E. The strength of weak learnability[J]. Machine Learning,1990, (2): 197-227
[135]Perrone M P, Cooper L N. When Networks Disagree:Ensemble Method for Neural Networks. In:Mammone R J ed. Artificial Neural Networks for Speech and Vision, London:Chapman-Hall,1993:126-142
[136]Breiman L. Bagging Predictors [J]. Machine Learning,1996, (2):123-140
[137]Zhou Z H, Wu J X, Tang W. Ensembling Neural Networks:Many Could Be Better Than All[J]. Artificial Intelligence,2002, (137):239-263
[138]Woods K, Kegelmeyer W P, Bowyer K. Combination of Multiple Classifiers Using Local Accuracy Estimates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(4):405-410
[139]Ho T K. The Random Subspace Method for Constructing Decision Forests [J]. IEEE Transactions on pattern Analysis and Machine Intelligence,1998, (8):832-844
[140]Dietterich T G. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:Bagging, Boosting, and Randomization[J]. Machine Learning,2000,40:139-157
[141]Dash M, Liu H. Consistency Based Search in Feature Selection[J]. Artificial Intelligence,2003,151:155-176
[142]Shimshoni Y, Intrator N. Classification of Seismic Signals by Integrating Ensembles of Nemal Networks[J]. IEEE Transactions on Signal Processing,1998,46(5):1194-1201
[143]Zhang X, Mesirov J, Waltz D. Hybrid System for Protein Secondary Structure Prediction[J]. Journal of Molecular Biology,1992,225:1049-1063
[144]Sharkey A J C,Sharkey N E, Cross S S. Adapting an Ensemble Approach for the Diagnosis of Breast Cancer[C]. Proceedings of International Conference on Artificial Neural Networks, Skovde, Sweden,1998:281-286
[145]Schenker A, Bunke H, Last M, Kandel A. Building Graph-based Classifier Ensembles by Random Node Selection[J]. Roli F. Kittler J, Windeatt T.(Eds.)MCS LNCS, 2004:214-222
[146]Krogh A, Vedelsby J. Neural Network Ensembles, Cross Validation, and Active Learning[J]. in:Touretzky D, Leen T. Advances in Neural Information Processing Systems,vol.7,MIT Press, Cambridge, MA,1995:231-238
[147]Partridge D. Network Generalization Differences Quantified [J]. Neural Networks, 1996, (9):263-271
[148]Partridge D, Yates W B. Engineering Multiversion Neural-net Systems[J]. Neural Computation,1996, (8):869-893
[149]Shipp C A, Kuncheva L I. Relationships Between Combination Methods and Measures of Diversity in Combining Classifiers[J]. Information Fusion,2002, (3):135-148
[150]Banfield R E, Hall L O, Bowyer K W, Kegelmeyer W P. Ensemble Diversity Measures and Their Application to Thinning[J]. Information Fusion.2005. (6):49-62
[151]Ruta D, Gabrys B. Classifier Selection for Majority Voting[J]. Information Fusion, 2005, (6):63-81
[152]Tsymbal A, Pechenizkiy M, Cunningham P. Diversity in Search Strategies for Ensemble Feature Selection[J]. Information Fusion,2005, (6):83-98
[153]Opitz D, Maclin R. Popular Ensemble Methods:An empirical study [J]. Journal of Artificial Intelligence Research,1999,11:169-198
[154]庄进发,罗键,彭彦卿,黄春庆,吴长庆.基于改进随机森林的故障诊断方法研究[J].计算机集成制造系统,2009,15(4)：777-785
[155]Breiman L. Random Forests[J]. Machine Learning,2001,45(1):5-32
[156]Lippmann R P. An Introduction to Computing with Neural Nets[J]. IEEE ASSP Magazine,1987, (4):4-22
[157]Cyberko G. Approximations by Superpositions of a Sigmoidal Function[M]. Math Control Signal System,1989
[158]Hema Rao, Alexey G. Ivakhnenko, Inductive Leaning Algorithms for Complex System Modeling[M]. CRC Press, Inc,1994
[159]Kuncheva L, Whitaker C. Measures of Diversity in Classifier Ensembles and Their Relationship with Ensemble Accuracy [J]. Machine Learning,2003,51(2):181-207
[160]Gunter S.and Bunkfe H. Feature Selection Algorithms for the Generation of Multiple Classifier Systems and Their Application to Handwritten Word Recognition[J]. Pattern Recognition Letters,2004, (25):1323-1336
[161]Maimon O, Rokach L. Improving Supervised Learning by Feature Decomposition. Eiter T, Schewe K D. (Eds.) FoIKS, LNCS 2284,2002:178-196
[162]Last M, Bunke H, Kandel A. A Feature-based Serial Approach to Classifier Combination[J]. Pattern Analysis and Applications,2002,5:385-398
[163]Taguchi G, Jugulum R. New Trends in Multivariate Diagnosis[J]. Sankhya:The Indian Journal of Statistics,2000, (62):233-248
[164]Jugulum R, Taguchi G. Direction of Abnormals and Treatment of Noise Factors in Multidimensional Systems:A Discussion[J]. Journal of Quality Engineering Forurn,2001, (3):37-44
[165]郑称德,韩之俊.MTS设计原理及其设计模型[J].管理工程学报,2000,(3)：43-46
[166]Mahalakshmi P, Ganesan K. Mahalanobis Taguchi System Based Criteria Selection for Shrimpaquaculture Development[J]. Computers and Electronics in Agriculture,2009, 65:192-197
[167]Huang C L, Hsu T S, Liu C M. The Mahalanobis-Taguchi System-Neural Network Algorithm for Data-mining in Dynamic Environments[J]. Expert Systems with Applications.2009,36:5475-5480
[168]Lee Yu-Cheng, Teng Hsiao-Lin. Predicting the Financial Crisis by Mahalanobis-Taguchi System-Examples of Taiwan's Electronic Sector[J]. Expert Systems with Applications,2009,36:7469-7478
[169]薜跃,韩之俊,盛党红.MTS法用于上市公司财务质量评估初探[J].数理统计与管理,2005,24(1)：81-85
[170]宗鹏,曾凤章.基于MTS的企业可持续发展评价体系研究[J].科学技术与工程,2006,6(8)：1163-1166
[171]王洪祥,韩之俊,李晓萍.基于MTS的县级市城市化评价[J].城市发展研究,2009,16(7)：33-38
[172]李国正,李丹.集成学习中特征选择技术[J］.上海大学学报(自然科学版),2007,(10)：598-604
[173]凌锦江,陈兆乾,周志华.基于特征选择的神经网络集成方法[J].复旦学报(自然科学版),2004,(10)：685-688
[174]张东波,黄辉先,王耀南.基于粗糙集约简的特征选择神经网络集成技术[J]. 控制与决策,2010,(3)：371-377
[175]Bryll R, Gutierrez O R, Quek F. Attribute Bagging:Improving Accuracy of Classifier Ensembles by Using Random Feature Subset[J]. Pattern Recognition,2003,36(3): 1291-1302
[176]Widmer G, Kubat M. Learning in the Presence of Concept Drift and Hidden Contexts[J]. Machine Learning,1996,23(1):69-101
[177]柴玉梅,周驰,王黎明.数据流上概念漂移的检测和分类[J].小型微型计算机系统,2011,32(3)：421-425
[178]Gao J, Ding B, Fan W, Han J, Yu P S. Classifying Data Streams with Skewed Class Distributions and Concept Drifts[J]. IEEE Internet Computing,2008,12(6):37-49
[179]Widmer G, Kubat M. Effective Learning in Dynamic Environments by Explicit Context Tracking[C]. Proceeding of the 6th European Conference on Machine Learning, LNCS667,1993:227-243
[180]钟玉峰,雷国华.一种基于滑动窗口技术的入侵检测方法[J].信息技术,2009,7：166-170
[181]Delanya S J, Cunninghamb P, Tsymbalb A, Coyle L. A Case-based Technique for Tracking Concept Drift in Spam Filtering[J]. Knowledge-Based Systems,2005, (18): 187-195
[182]孙洁,李辉,韩建光.基于滚动时间窗口支持向量机的财务困境预测动态建模[J].管理工程学报,2010,24(4)：174-180
[183]Sun J, Li H. Dynamic Financial Distress Prediction Using Instance Selection for the Disposal of Concept Drift[J]. Expert Systems with Applications,2011, (38):2566-2576
[184]Sun J, He K Y, Li H. SFFS-PC-NN Optimized by Genetic Algorithm for Dynamic Prediction of Financial Distress with Longitudinal Data Streams[J]. Knowledge-Based Systems,2001, (3):1-11
[185]Polikar R, Udpa L, Udpa S S, et al. Learn++:An Incremental Learning Algorithm for Supervised Neural Networks[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part C:Applications and Reviews,2001, (4):497-500
[186]Schlimmer J C, Granger R H. Incremental Learning from Noisy Data[J]. Machine Learning,1986, (1):317-354
[187]Street W N, Kim Y S. A Streaming Ensemble Algorithm for Large-scale Classification[C]. Proceeding of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2001:377-382
[188]Wang H, Fan W, Yu P S, Han J. Mining Concept-drifting Data Streams Using Ensemble Classifiers[C]. Proceeding of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2003, (8):24-27
[189]Sun J, Li Hui, Zhang Meng. Multiple Classifiers Hybrid Combination For Companies' Financial Distress Prediction[J]. Systems Engineering Theory and Practice, 2009,29(2):78-86