Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy
作者： Philippe du Jardin
DOI: 10.1016/J.NEUCOM.2009.11.034
关键词: Classification methods 、 Data mining 、 Computer science 、 Structure (mathematical logic) 、 Machine learning 、 Type I and type II errors 、 Artificial intelligence 、 Reduction (complexity) 、 Feature selection 、 Set (abstract data type) 、 Artificial neural network 、 Bankruptcy
摘要: We evaluate the prediction accuracy of models designed using different classification methods depending on technique used to select variables, and we study relationship between structure their ability correctly predict financial failure. show that a neural network based model set variables selected with criterion it is adapted leads better results than chosen criteria in literature. also way which may represent profiles healthy companies plays role Type I error reduction.
core.ac.uk
elsevier.com
sci-hub.se 参考文章(75)
Meziane Yacoub, Y. Bennani, HVS : A Heuristic for Variable Selection in Multilayer Artificial Neural Network Classifier Intelligent Engineering Systems Through Artificial Neural Networks, St. Louis, Missouri. ,vol. 7, pp. 527- 532 ,(1997)
Arthur H. Winakor, Raymond Smith, Changes in the financial structure of unsuccessful industrial corporations University of Illinois. ,(1935)
Heather Mitchell, Juliana Yim, A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis Nova Economia. ,vol. 15, pp. 73- 93 ,(2005)
Choong Nyoung Kim, Raymond McLeod, Expert, linear models, and nonlinear models of expert decision making in bankruptcy prediction: a lens model analysis Journal of Management Information Systems. ,vol. 16, pp. 189- 206 ,(1999) , 10.1080/07421222.1999.11518239
Erkki K. Laitinen, Teija Laitinen, Bankruptcy prediction: Application of the Taylor's expansion in logistic regression International Review of Financial Analysis. ,vol. 9, pp. 327- 349 ,(2000) , 10.1016/S1057-5219(00)00039-9
George H John, Ron Kohavi, Karl Pfleger, None, Irrelevant Features and the Subset Selection Problem Machine Learning Proceedings 1994. pp. 121- 129 ,(1994) , 10.1016/B978-1-55860-335-6.50023-4
Ron Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection international joint conference on artificial intelligence. ,vol. 2, pp. 1137- 1143 ,(1995)
E DorseyRobert, D JohnsonJohn, O EdmisterRobert, Bankruptcy Prediction Using Artificial Neural Systems The Research Foundation of the Institute of Chartered Financial Analysts. ,(2007)
Indranil Bose, Raktim Pal, Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach European Journal of Operational Research. ,vol. 174, pp. 959- 982 ,(2006) , 10.1016/J.EJOR.2005.05.009
Linda M. Salchenberger, E. Mine Cinar, Nicholas A. Lash, Neural Networks: A New Tool for Predicting Thrift Failures Decision Sciences. ,vol. 23, pp. 899- 916 ,(1992) , 10.1111/J.1540-5915.1992.TB00425.X