Improving Financial Bankruptcy Prediction Using Oversampling Followed by Fuzzy Rough Feature Selection via Evolutionary Search

[1]  Stewart Jones.  Corporate bankruptcy prediction: a high dimensional analysis , 2017, Review of Accounting Studies.

[2]  Paola Cerchiello,et al.  Deep Learning for Assessing Banks’ Distress from News and Numerical Financial Data , 2018 .

[3]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[4]  Mu-Chen Chen,et al.  Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..

[5]  Wei Chen,et al.  Ensemble learning with label proportions for bankruptcy prediction , 2020, Expert Syst. Appl..

[6]  Anoop Kumar Tiwari,et al.  Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection , 2020, Soft Computing.

[7]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[8]  Vikas Kumar,et al.  Systematic review of bankruptcy prediction models: Towards a framework for tool selection , 2018, Expert Syst. Appl..

[9]  Jakub M. Tomczak,et al.  Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction , 2016, Expert Syst. Appl..

[10]  Yi-Chung Hu,et al.  A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems , 2019, Soft Computing.

[11]  Jodi L. Gissel,et al.  A Review of Bankruptcy Prediction Studies: 1930 to Present , 2006 .

[12]  Fariborz Ahmadi,et al.  New Approach to Bankruptcy Prediction using Genetic Algorithm , 2012 .

[13]  Qiang Shen,et al.  Tolerance-based and Fuzzy-Rough Feature Selection , 2007, 2007 IEEE International Fuzzy Systems Conference.

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[15]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[16]  Dechang Pi,et al.  The Prediction of Protein-Protein Interaction Sites Based on RBF Classifier Improved by SMOTE , 2014 .

[17]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[18]  Makram Soui,et al.  Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE , 2020, Information Systems Frontiers.

[19]  Hossam Faris,et al.  Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market , 2019, Progress in Artificial Intelligence.

[20]  Richard Jensen,et al.  Towards scalable fuzzy-rough feature selection , 2015, Inf. Sci..

[21]  To FinTech and Beyond , 2019, SSRN Electronic Journal.

[22]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[23]  Paola Cerchiello,et al.  Deep learning bank distress from news and numerical financial data , 2017, ArXiv.

[24]  Anoop Kumar Tiwari,et al.  A fitting model based intuitionistic fuzzy rough feature selection , 2020, Eng. Appl. Artif. Intell..

[25]  Herbert Kimura,et al.  Machine learning models and bankruptcy prediction , 2017, Expert Syst. Appl..

[26]  Vadlamani Ravi,et al.  Failure prediction of dotcom companies using hybrid intelligent techniques , 2009, Expert Syst. Appl..

[27]  Bo Wang,et al.  Learning with label proportions based on nonparallel support vector machines , 2017, Knowl. Based Syst..

[28]  Leila Bateni,et al.  Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis , 2020 .

[29]  E. Laitinen,et al.  A Race for Long Horizon Bankruptcy Prediction , 2020 .

[30]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[31]  R. Jarrow,et al.  Bankruptcy Prediction With Industry Effects , 2004 .

[32]  Yaojin Lin,et al.  A graph approach for fuzzy-rough feature selection , 2020, Fuzzy Sets Syst..

[33]  Anoop Kumar Tiwari,et al.  Enhanced Prediction of plant virus-encoded RNA silencing suppressors by incorporating Reduced Set of Sequence Features using SMOTE followed by Fuzzy-Rough Feature Selection Technique , 2019, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[34]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.

[35]  Shigeo Aoki,et al.  Bankruptcy Prediction Using Decision Tree , 2004 .

[36]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[37]  Yan Yu,et al.  Variable selection and corporate bankruptcy forecasts , 2015 .

[38]  Jiye Liang,et al.  Fuzzy-rough feature selection accelerator , 2015, Fuzzy Sets Syst..

[39]  Tyler Shumway.  Forecasting Bankruptcy More Accurately: A Simple Hazard Model , 1999 .

[40]  Didier Dubois,et al.  Putting Rough Sets and Fuzzy Sets Together , 1992, Intelligent Decision Support.

[41]  P. Drotár,et al.  Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets , 2020 .

[42]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[43]  David R. Musicant,et al.  Learning from Aggregate Views , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[44]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[45]  Deron Liang,et al.  Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study , 2016, Eur. J. Oper. Res..

[46]  L. Green,et al.  Area under the curve as a measure of discounting. , 2001, Journal of the experimental analysis of behavior.

[47]  Jia Zhai,et al.  A two‐stage Bayesian network model for corporate bankruptcy prediction , 2020, International Journal of Finance & Economics.