Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods. Information Sciences. 2021, 559.
战略差异度对企业技术创新的影响：代理成本的中介作用. 科技进步与对策. 2021, 38(6). (CSSCI检索)
Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Information Fusion. 2020, 54. (SSCI/SCI/EI检索, ESI热点, ESI高被引)
股票风险警示对同行企业盈余管理行为的影响研究. 财经理论与实践. 2020, 41(5). (CSSCI检索)
行业竞争、战略差异度与企业金融化. 当代财经. 2020, (12). (CSSCI检索)
Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry. Risk Management. 2019, 21. (SSCI检索)
Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Information Sciences. 2018, 425. (SSCI/SCI/EI检索, ESI高被引)
Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowledge-Based Systems. 2017, 120. (SSCI/SCI/EI检索)
The dynamic financial distress prediction method of EBW-VSTW- SVM. Enterprise Information Systems. 2016, 10(6). (SSCI/ SCI/EI检索)
Combining B&B-based hybrid feature selection and the imbalance-oriented multiple-classifier ensemble for imbalanced credit risk assessment. Technological and Economic Development of Economy. 2015, 21(3). (SSCI检索)
Dynamic credit scoring using B & B withincremental-SVM-ensemble. Kybernetes. 2015, 44(4). (SSCI检索)
Imbalance-oriented SVM methods for financial distress prediction: a comparative study among the new SB-SVM-ensemble method and traditional methods. Journal of the Operational Research Society. 2014, 65(12). (SSCI检索)
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems. 2014, 5. (SSCI/SCI/EI检索)
Integration of batch weighted method with classifiers combination to solve financial distress prediction concept drift. 7th International Joint Conference on Computational Sciences and Optimization. 2014.7.4-2014.7.6. (EI检索)
Sensitivity of decision tree algorithm to class-imbalanced bank credit risk early warning. 7th International Joint Conference on Computational Sciences and Optimization. 2014.7.4-2014.7.6. (EI检索)
Concept drift oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction. IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems. 2013, 43(4). (SSCI/SCI/EI检索)
AdaBoost and Bagging ensemble approaches with neural network as base learner for financial distress prediction of Chinese construction and real estate companies. Recent Patents on Computer Science. 2013, 6(1). (EI检索)
Forecasting business failure using two-stage ensemble ofmultivariate discriminant analysis and logistic regression. Expert Systems. 2013, 30(5). (SSCI/SCI/EI检索)
Predicting business failure using an RSF-based case-basedreasoning ensemble forecasting method. Journal of Forecasting. 2013, 32(2). (SSCI检索)
Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms, International Journal of Systems Science, 2013, 44(8): 1409~1425. (SCI/EI检索)
Financial distress prediction using support vector machines: Ensemble vs. individual. Applied Soft Computing. 2012, 12(8). (SSCI/SCI/EI检索)
Integration of random sample selection, support vector machine and ensemble for financial risk forecasting with an empirical analysis on the necessity of feature selection. Intelligent Systems in Accounting, Finance and Management. 2012, 19(4).
Forecasting business failure: The use of nearest-neighbour support vectors and correctingimbalanced samples – Evidence from the Chinese hotel industry. Tourism Management. 2012, 33(3). (SSCI检索)
Dynamic financial distress prediction using instance selection for the disposal of concept drift. Expert Systems with Applications. 2011, 38(3). (SSCI/SCI/EI检索)
SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams. Knowledge-Based Systems. 2011, 24(7). (SCI/EI检索)
AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies. Expert Systems with Applications. 2011, 38(8). (SSCI/SCI/EI检索)
Principal component case-based reasoning ensemble for business failure prediction. Information & Management. 2011, 48(6). (SCI/EI检索)
Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Computers & Operations Research, 2011, 38(2). (SCI/EI检索)
基于滚动时间窗口支持向量机的财务困境预测动态建模. 管理工程学报. 2010, 24(4). (CSSCI检索)
Forecasting business failure in China using case-based reasoning with hybrid case representation. Journal of Forecasting. 2010, 29(5). (SSCI/EI检索)
Financial distress early warning based on group decision making. Computers & Operations Research. 2009, 36(3). (SSCI/SCI/EI检索)
Financial distress prediction based on serial combination of multiple classifiers. Expert Systems with Applications. 2009, 36(4). (SSCI/SCI/EI检索)
企业财务困境的多分类器混合组合预测. 系统工程理论与实践. 2009, (2). (EI/CSSCI检索)
遗传算法优化灰色案例推理的财务困境预测. 科研管理. 2009, 30(02). (CSSCI检索)
Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II. European Journal of Operational Research. 2009, 197(1). (SCI/EI检索)
Gaussian case-based reasoning for business failure prediction with empirical data in China. Information Sciences. 2009, 179(1-2). (SSCI/SCI/EI检索)
Data mining method for listed companies' financial distress prediction. Knowledge-Based Systems. 2008, 21(1). (SCI/EI检索)
Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications. 2008, 35(3). (SCI/EI检索)
Financial distress prediction based on similarity weighted voting CBR. Lecture Notes in Computer Science. 2006, 4093. (SCI/EI检索)
