| Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches J Sun, H Li, QH Huang, KY He Knowledge-Based Systems 57, 41-56, 2014 | 486 | 2014 |
| Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates J Sun, J Lang, H Fujita, H Li Information Sciences 425, 76-91, 2018 | 380 | 2018 |
| Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting J Sun, H Li, H Fujita, B Fu, W Ai Information Fusion 54, 128-144, 2020 | 279 | 2020 |
| Data mining method for listed companies’ financial distress prediction J Sun, H Li Knowledge-Based Systems 21 (1), 1-5, 2008 | 257 | 2008 |
| Ranking-order case-based reasoning for financial distress prediction H Li, J Sun Knowledge-based systems 21 (8), 868-878, 2008 | 200 | 2008 |
| Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods H Li, J Sun, J Wu Expert Systems with Applications 37 (8), 5895-5904, 2010 | 198 | 2010 |
| Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble J Sun, H Fujita, P Chen, H Li Knowledge-Based Systems 120, 4-14, 2017 | 189 | 2017 |
| Financial distress prediction using support vector machines: Ensemble vs. individual J Sun, H Li Applied Soft Computing 12 (8), 2254-2265, 2012 | 187 | 2012 |
| AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies J Sun, M Jia, H Li Expert systems with applications 38 (8), 9305-9312, 2011 | 150 | 2011 |
| Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers J Sun, H Li Expert Systems with Applications 35 (3), 818-827, 2008 | 140 | 2008 |
| Financial distress early warning based on group decision making J Sun, H Li Computers & Operations Research 36 (3), 885-906, 2009 | 138 | 2009 |
| Predicting business failure using multiple case-based reasoning combined with support vector machine H Li, J Sun Expert systems with applications 36 (6), 10085-10096, 2009 | 132 | 2009 |
| Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction H Li, H Adeli, J Sun, JG Han Computers & Operations Research 38 (2), 409-419, 2011 | 129 | 2011 |
| Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples–Evidence from the Chinese hotel industry H Li, J Sun Tourism Management 33 (3), 622-634, 2012 | 127 | 2012 |
| Gaussian case-based reasoning for business failure prediction with empirical data in China H Li, J Sun Information Sciences 179 (1-2), 89-108, 2009 | 127 | 2009 |
| Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors H Li, J Sun, BL Sun Expert Systems with Applications 36 (1), 643-659, 2009 | 124 | 2009 |
| An application of support vector machine to companies’ financial distress prediction XF Hui, J Sun Modeling Decisions for Artificial Intelligence: Third International …, 2006 | 96 | 2006 |
| Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods J Sun, H Fujita, Y Zheng, W Ai Information Sciences 559, 153-170, 2021 | 87 | 2021 |
| Majority voting combination of multiple case-based reasoning for financial distress prediction H Li, J Sun Expert Systems with Applications 36 (3), 4363-4373, 2009 | 87 | 2009 |
| Financial distress prediction based on serial combination of multiple classifiers J Sun, H Li Expert Systems with Applications 36 (4), 8659-8666, 2009 | 85 | 2009 |
Hojjat AdeliThe Ohio State UniversityVerified email at osu.edu
