姓名：孙洁
职称：教授
电子邮箱：sunjiehit@gmail.com
个人简介：
天津财经大学会计学院教授、博士生导师、管理学博士、中国注册会计师（非职业会员）。主要研究方向为：企业财务预警、企业信用评估、会计信息化与智能化等。曾入选天津市高校学科领军人才培养计划、天津市131创新型人才培养工程第一层次、天津市131创新型人才培养工程第二层次、浙江省151人才工程第三层次、浙江省五星级青年教师、浙江省之江青年社科****。2012年6-9月，曾赴韩国东国大学担任客座教授。
先后主持国家自然科学基金项目3项、省部级科研项目4项，参与国家级项目3项、省部级项目1项。出版学术专著1部，并在国内外知名刊物和国际会议上发表论文50余篇，其中，ESI高被引论文1篇，SSCI/SCI收录论文37篇，EI收录论文8篇，国家自然科学基金委管理学部重要期刊6篇。发表期刊包括：Information Sciences、IEEE Transactions on Systems, Man, and Cybernetics - Systems、Enterprise Information Systems、Journal of the Operational Research Society、European Journal of Operational Research、Knowledge-Based Systems、Applied Soft Computing、Technological and Economic Development of Economy、Tourism Management、Information & Management、Computers & Operations Research、Journal of Forecasting、Risk Management、Intelligent System in Accounting, Finance & Management、Information & Management、Expert Systems with Applications、Expert Systems、International Journal of Systems Science、系统工程理论与实践（中英文版）、管理工程学报、科研管理、中国管理科学、中国软科学等。截至2019年3月，所发表论文在Web of Science的SSCI/SCI数据库的他引次数为592次，在Google Scholar数据库的他引次数为1773次，在中国知网（CNKI）数据库的他引次数为1993次。

教育背景：
2004-2007哈尔滨工业大学管理学院技术经济及管理博士
2002-2004 哈尔滨工业大学管理学院会计学硕士
1998-2002 哈尔滨工业大学管理学院会计学学士

工作简历：
2016—至今天津财经大学会计学院教授
2013—2016浙江师范大学经济与管理学院教授
2008—2013浙江师范大学经济与管理学院副教授
2007—2008浙江师范大学经济与管理学院讲师

研究方向：
企业财务预警、企业信用评估、会计信息化、财务智能化

主要课程：
计算机财务管理、会计信息系统

发表论文：
1. 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)
2. 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)
3. The dynamic financial distress prediction method of EBW-VSTW- SVM. Enterprise Information Systems. 2016, 10(6). (SSCI/ SCI/EI检索)
4. 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)
5. Dynamic credit scoring using B & B withincremental-SVM-ensemble. Kybernetes. 2015, 44(4). (SSCI检索)
6. 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)
7. 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)
8. 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)
9. 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)
10. 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)
11. 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)
12. Forecasting business failure using two-stage ensemble ofmultivariate discriminant analysis and logistic regression. Expert Systems. 2013, 30(5). (SSCI/SCI/EI检索)
13. Predicting business failure using an RSF-based case-basedreasoning ensemble forecasting method. Journal of Forecasting. 2013, 32(2). (SSCI检索)
14. 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)
15. Financial distress prediction using support vector machines: Ensemble vs. individual. Applied Soft Computing. 2012, 12(8). (SSCI/SCI/EI检索)
16. 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).
17. 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)
18. 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)
19. 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)
20. 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)
21. Principal component case-based reasoning ensemble for business failure prediction. Information & Management. 2011, 48(6). (SCI/EI检索)
22. Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Computers & Operations Research, 2011, 38(2). (SCI/EI检索)
23. 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)
24. Forecasting business failure in China using case-based reasoning with hybrid case representation. Journal of Forecasting. 2010, 29(5). (SSCI/EI检索)
25. Financial distress early warning based on group decision making. Computers & Operations Research. 2009, 36(3). (SSCI/SCI/EI检索)
26. Financial distress prediction based on serial combination of multiple classifiers. Expert Systems with Applications. 2009, 36(4). (SSCI/SCI/EI检索)
27. 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)
28. 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)
29. 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)
30. Gaussian case-based reasoning for business failure prediction with empirical data in China. Information Sciences. 2009, 179(1-2). (SSCI/SCI/EI检索)
31. Data mining method for listed companies' financial distress prediction. Knowledge-Based Systems. 2008, 21(1). (SCI/EI检索)
32. 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)
33. Financial distress prediction based on similarity weighted voting CBR. Lecture Notes in Computer Science. 2006, 4093. (SCI/EI检索)

出版著作：
企业财务危机预警的智能决策方法. 中国社会科学出版社, 2013

研究项目：
1.国家自然科学基金面上项目：多类别非平衡企业信用评估的多SVM集成建模研究，2018.01-2021.12，主持
2.国家自然科学基金面上项目：基于类别非平衡时序增量数据批的多SVM动态集成企业信用评估建模，2014.01-2017.12，主持
3.国家自然科学基金青年基金项目：面向纵横数据流概念漂移的企业财务困境预测动态建模研究，2009.01-2011.12，主持
4.教育部人文社科基金青年基金项目：基于时序赋权样例选择的多支持向量机动态集成企业信用评估研究，2013.01-2016.01，主持
5.浙江省自然科学基金一般项目：类别非平衡财务困境预测建模的多SVM集成方法，2014.01-2015.12，主持
6.浙江省哲学社会科学规划课题：基于滚动时间窗口的增量支持向量回归机股指动态预测方法及实证研究，2012.01-2014.06，主持
7.浙江省自然科学基金项目：基于支持向量机批增量学习的银行信贷决策动态建模研究，2010.01-2011.12，主持
8.国家自然科学基金面上项目：基于多源多维随机融合核案例推理的信用违约互换风险预测，2012.01-2015.12，参与
9.国家自然科学基金青年基金项目：权重国际贸易网络的经验和建模研究，2008.01-2010.12，参与
10.国家自然科学基金面上项目：上市公司信息披露与资本成本研究，2006.01-2008.12，参与
11.浙江省自然科学基金项目：供应链企业合作健康诊断与协调研究，2008.01-2009.12，参与

荣誉及获奖：
1. 省高等教育研究成果一等奖，2017.
2. 省高等教育教学成果二等奖，2016.
3. 教育部高等学校科学研究优秀成果奖（人文社会科学）三等奖，2015.
4. 省哲学社会科学优秀成果奖二等奖，2014.
5. 省社科联青年社会科学优秀成果奖三等奖，2014.
6. 省经济学会优秀成果奖二等奖，2013.
7. 省高等学校科研成果奖二等奖，2012.
8. 省社科联青年社会科学优秀成果奖三等奖，2012.
9. 省生产力学会优秀成果奖三等奖，2011.
10. 省高校科研成果奖二等奖，2010.