A Combination of Models for Financial Crisis Prediction: Integrating Probabilistic Neural Network with Back-Propagation based on Adaptive Boosting
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[1] Edward I. Altman,et al. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .
[2] Fernando Fernández-Rodríguez,et al. Forecasting Financial Failure of Firms via Genetic Algorithms , 2013, Computational Economics.
[3] Hui Li,et al. Financial distress prediction based on serial combination of multiple classifiers , 2009, Expert Syst. Appl..
[4] James A. Ohlson. FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .
[5] W. Beaver. Financial Ratios As Predictors Of Failure , 1966 .
[6] Roberto Kawakami Harrop Galvão,et al. Neural and Wavelet Network Models for Financial Distress Classification , 2005, Data Mining and Knowledge Discovery.
[7] Loris Nanni,et al. An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring , 2009, Expert Syst. Appl..
[8] Qiang Wang,et al. Analyzing financial distress of listed companies using neural network , 2012, 2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization.
[9] Peng Luo,et al. A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation , 2014, Int. J. Comput. Intell. Syst..
[10] Hui Li,et al. Statistics-based wrapper for feature selection: An implementation on financial distress identification with support vector machine , 2014, Appl. Soft Comput..
[11] Ping-Feng Pai,et al. Incorporating support vector machines with multiple criteria decision making for financial crisis analysis , 2012, Quality & Quantity.
[12] Mehdi Khashei,et al. Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs) , 2012, Comput. Ind. Eng..
[13] Sofie Balcaen,et al. 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems , 2006 .
[14] David C. Yen,et al. A comparative study of classifier ensembles for bankruptcy prediction , 2014, Appl. Soft Comput..
[15] Ligang Zhou. Predicting the Removal of Special Treatment or Delisting Risk Warning for Listed Company in China with Adaboost , 2013, ITQM.
[16] Shumin Fei,et al. Probability estimation for multi-class classification using AdaBoost , 2014, Pattern Recognit..
[17] C. Zavgren. ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS , 1985 .
[18] Lu Wang,et al. Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function , 2016, J. Intell. Syst..
[19] Mingliang Wang,et al. Prediction of Banking Systemic Risk Based on Support Vector Machine , 2013 .
[20] Arindam Chaudhuri,et al. Fuzzy Support Vector Machine for bankruptcy prediction , 2011, Appl. Soft Comput..
[21] F. Economie. 35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems , 2004 .
[22] Zied Elouedi,et al. Classification systems based on rough sets under the belief function framework , 2011, Int. J. Approx. Reason..
[23] Ramesh Sharda,et al. A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[24] Jie Xu,et al. An Improved Back Propagation Neural Network Model and Its Application , 2014, J. Comput..
[25] Ligang Zhou. Predicting listing status of listed companies in China using adaboost approach , 2014, 2014 10th International Conference on Natural Computation (ICNC).
[26] Donald F. Specht,et al. Probabilistic neural networks , 1990, Neural Networks.
[27] Jinyong Yang,et al. AdaBoost based bankruptcy forecasting of Korean construction companies , 2014, Appl. Soft Comput..
[28] Jonathan L. Ticknor. A Bayesian regularized artificial neural network for stock market forecasting , 2013, Expert Syst. Appl..
[29] Yi-Bo Luo,et al. Using the Back Propagation Neural Network Approach to Bias Correct TMPA Data in the Arid Region of Northwest China , 2014 .