计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 1-10.doi: 10.11896/j.issn.1002-137X.2019.02.001
• 大数据与数据科学 • 下一篇
刘颖
LIU Ying
摘要: 半结构、非结构化、海量的供应链金融数据使得大数据环境下金融数据分析的模式和方法相对复杂。面向大数据样本研究,如何将大样本相比于小样本的独有特征体现在分类模型中值得深入探索。文中从供应链金融数据分布特征入手,分析影响信用风险分类模型的主要因素;对多年来的相关研究成果进行归类分析,概括信用数据分布特征,包括信用数据非均衡与不对称性、信用数据噪声和离群点的存在以及信用数据的非线性多维特征,并探讨了进一步的解决策略。供应链金融大数据分布特征的分析旨在助力挖掘隐含在海量金融数据背后的知识信息,为信用风险模型的构建奠定了坚实的基础。
中图分类号:
[1]TRUONG N,LI Z,VIRGINIA S,et al.Big data analytics in supply chain management:A state-of-the-art literature review[J].Computers and Operations Research,2018,98:254-264. [2]RICHARD L V,MATTHEW E,CARL W O.Big data:What it is and why you should care[M].IDC Go-to-Market Services,2011. [3]RICHARD A T,PETRI T H.Big data applications in operations/supply-chain management:A literature review[J].Computers & Industrial Engineering,2016,101:528-543. [4]GANTZ J,REINSEL D.Extracting value from chaos[M].IDC Go-to-Market Services,2011:1-12. [5]HUANG Y Y,HANDFIELD R B.Measuring the benefits of ERP on supply management maturity model:a ‘big data’ me-thod[J].International Journal of Operation & Production Ma-nagement,2015,35 (1):2-25. [6]CHEN C L P,ZHANG C Y.Data-intensive applications,challenges,techniques and technologies:a survey on Big Data[J].Information Sciences,2014,275(11):314-347. [7]BABICEANU R F,SEKER R.Big Data and virtualization for manufacturing cyber-physical systems:a survey of the current status and future outlook[J].Computers in industry,2016,81:128-137. [8]CHAO L M,XING C X,ZHANG Y.Data Science Studies: State-of-the-art and Trends[J].Computer Science,2018,45(1):1-13.(in Chinese) 朝乐门,邢春晓,张勇.数据科学研究的现状与趋势[J].计算机科学,2018,45(1):1-13. [9]XU H L,TANG S,MAO R,et al.Various Pivots Based Outlier Dectection Algorithm in Metric Space[J].Chinese Journal of Computers,2017,40(12):2839-2855.(in Chinese) 许红龙,唐颂,毛睿,等.基于多种支撑点的度量空间离群检测算法[J].计算机学报,2017,40(12):2839-2855. [10]袁荃.基于供应链金融的中小企业融资决策研究[D].武汉:武汉大学,2010. [11]Demica Limited Company.Research report:A study on the growth of supply chain finance,as evidenced by SCF[EB/OL].http://www.demica.com. [12]XIAO J,XUE S T,HUANG J,et al.A Semi-Supervised Co-Training Model for Customer Credit Scoring[J].Chinese Journal of Management Science,2016,24(6):124-131.(in Chinese) 肖进,薛书田,黄静,等.客户信用评估半监督协同训练模型研究[J].中国管理科学,2016,24(6):124-131. [13]YANG J,ZHOU Y G.Credit risk spillovers among financial institutions around the global credit crisis:Firm-level evidence[J].Management Science,2013,59(10):2343-2359. [14]CHEN H,CHIANG R H,STOREY V C.Business intelligence and analytics:From big data to big impact[J].MIS Quarterly,2012,36(4):1165-1188. [15]ARCHENAA J,ANITA E M.A survey of big data analytics in healthcare and government[J].Procedia Computer Science,2015,50:408-413. [16]VATRAPU R,MUKKAMALA R R,HUSSAIN A,et al.Social set analysis:A set theoretical approach to big data analytics[J].IEEE Access,2016,4:2542-2571. [17]KHAN Z,ANJUM A,SOOMRO K,et al.Towards cloud based big data analytics for smart future cities[J].Journal of Cloud Computing,2015,4(1):2. [18]FIOSINA J,FIOSINS M,MULLER J P.Big data processing and mining for next generation intelligent transportation systems[J].Journal Teknologi,2013,63(3):21-38. [19]SLEDGIANOWSKI D,GOMAA M,TAN C.Toward integra- tion of Big Data,technology and information systems competencies into the accounting curriculum[J].Journal of Accounting Education,2017,38:81-93. [20]CERCHIELLO P,GIUDICI P.Big data analysis for financial risk management[J].Journal of Big Data,2016,3(1):1-12. [21]ZHAO N,ZHANG X F,ZHANG L J.Overview of Imbalanced Data Classification[J].Chinese Journal of Computers,2018,45(S1):22-27.(in Chinese) 赵楠,张小芳,张利军.不平衡数据分类研究综述[J].计算机科学,2018,45(S1):22-27. [22]DEBASHREE D,SAROJ K B,BISWAJIT P.Redundancy-dri- ven modified Tomek-link based undersampling:A solution to class imbalance[J].Pattern Recognition Letters,2017,93(1):3-12. [23]YANG Z,ABHISHEK K S,KWOK L T.Imbalanced classification by learning hidden data structure[J].IIE Transations,2016,48(7):614-628. [24]YI B H,ZHU J J,LI J.Imbalanced Data Classification on Micro-Credit Company Customer Credit Risk Assessment Using Improved SMOTE Support Vector Machine[J].Chinese Journal of Mangement Science,2016,24(3):24-30.(in Chinese) 衣柏衡,朱建军,李杰.基于改进SMOTE的小额贷款公司客户信用风险非均衡SVM分类[J].中国管理科学,2016,24(3):24-30. [25]PIERRI F,STANGHELLINI E,BISTONI N.Risk analysis and retrospective unbalanced data[J].Revstat-statistical Journal,2016,14(2):157-169. [26]LI S,SONG W F,QIN H,et al.Deep variance network:An ite- rative,improved CNN framework for unbalanced training datasets[J].Pattern Recognition,2018,81:294-308. [27]XIONG B Y,WANG G Y,DENG W B.Under-Sampling Method Based on Sample Weight for Imbalanced Data[J].Journal of Computer Research and Development,2016,53(11):2613-2622.(in Chinese) 熊冰妍,王国胤,邓维斌.基于样本权重的不平衡数据欠抽样方法[J].计算机研究与发展,2016,53(11):2613-2622. [28]CHICLANA F,MATA F,PEREZ L G,et al.Type-1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology:Application to Eurobonds Credit Risk Evaluation[J].International Journal of Intelligent Systems,2018,33(5):1071-1088. [29]VAPNIK.The nature of statistical learning theory [M].New York:Springer,1995:1-14. [30]SHAO Y H,CHEN W J,ZHANG J J,et al.An efficient weighted Lagrangian twin support vector machine for imbalanced data classification [J].Pattern Recognition,2014,47(9):3158-3167. [31]CHENG Y Q.Credit Rating of Small Enterprises Based on Unbalanced Data[J].Operations Research and Management Science,2016,25(6):181-189.(in Chinese) 程砚秋.基于不平衡数据的小企业信用风险评价[J].运筹与管理,2016,25(6):181-189. [32]GOMEZ C L,CAMPS V G,BRUZZONE L.Mean map kernel methods for semisupervised cloud classification[J].IEEE Tran-sactions on Geoscience and Remote Sensing,2010,48(1):207-220. [33]XIA Z G,XIA S X,CAI S Y,et al.Semi-supervised Gaussian process classification algorithm addressing the class imbalance[J].Journal on Communications,2013,34(5):42-51.(in Chinese) 夏战国,夏士雄,蔡世玉,等.类不均衡的半监督高斯过程分类算法[J].通信学报,2013,34(5):42-51. [34]LI X F,LI J,DONG Y F,et al.A New Learning Algorithm for Imbalanced Data-PCBoost[J].Chinese Journal of Computers,2012,35(2):202-209.(in Chinese) 李雄飞,李军,董元方,等.一种新的不平衡数据学习算法PC-Boost[J].计算机学报,2012,35(2):202-209. [35]LI K W,YANG L,LIU W Y,et al.Classification Method of Imbalanced Data Based on RSBoost[J].Computer Science,2015,42(9):249-252.(in Chinese) 李克文,杨磊,刘文英,等.基于RSBoost算法的不平衡数据分类方法[J].计算机科学,2015,42(9):249-252. [36]ZHU B,HE C Z,LI H Y.Research on Credit Scoring Model Based on Transfer Learning[J].Operations Research and Ma-nagement Science,2015,24(2):201-207.(in Chinese) 朱兵,贺昌政,李慧媛.基于迁移学习的客户信用评估模型研究[J].运筹与管理,2015,24(2):201-207. [37]CHANG Y C,CHANG K H,CHU H H,et al.Establishing decision tree-based short-term default credit risk assessment mo-dels[J].Communications in Statistics-theory and Methods,2016,45(23):6803-6815. [38]SUN J,LEE Y C,LI H,et al.Combining B&B-based hybrid feature selection and the imbalance-oriented multiple-classifier ensemble for imbalanced credit risk assessment[J].Technological and Economic Development of Economy,2015,21(3):351-378. [39]LIU F,MAO Z Z,LI L.Outlier detection for control process data based on fuzzy ARHMM[J].Chinese Journal of Scientific Instrument,2010,31(5):984-990.(in Chinese) 刘芳,毛志忠,李磊.基于模糊自回归隐马尔可夫模型的控制过程异常数据检测[J].仪器仪表学报,2010,31(5):984-990. [40]GRACES H,SBARBARO D.Outliers detection in environmental monitoring databases[J].Engineering Application of Artificial Intelligence,2011,24(2):341-349. [41]JIA R D,LIU J H,MAO Z Z,et al.Outlier detection for batch processes based on robust M-estimation[J].Chinese Journal of Scientific Instrument,2013,34(8):1726-1731.(in Chinese) 贾润达,刘俊豪,毛志忠,等.基于鲁棒M估计的间歇过程离群点检测[J].仪器仪表学报,2013,34(8):1726-1731. [42]JIANG Z,ZHAN Y Z.Noise control and related algorithm for semi-supervised classification[J].Journal of Jiangsu University(Natural Science Edition),2015,36(4):435-438.(in Chinese) 姜震,詹永照.半监督分类中的噪声控制及相关算法[J].江苏大学学报(自然科学版),2015,36(4):435-438. [43]WU J H,ZHANG Y,WANG X J.The Measurement Study of Corporate Bond Default Risk under the Information Disclosure Distortion[J].Jouranl of Applied Statistics and Management,2017,36(1):175-190.(in Chinese) 吴建华,张颖,王新军.信息披露扭曲下企业债券违约风险量化研究[J].数理统计与管理,2017,36(1):175-190. [44]JIANG M F,TSENG S S,SU C M.Two-phase clustering process for outliers detection[J].Pattern Recognition Letters,2001,22(6-7):691-700. [45]ZHUANG H,ZHANG J,BROVA G,et al.Mining query-based subnetwork outliers in heterogeneous information networks[C]∥IEEE International Conference on Data Mining,Piscataway.NJ:IEEE,2014:1127-1132. [46]ZHU L,QIU Y Y,YU S,et al.A Fast KNN-Based MST Outlier Detection Method Chinese[J].Journal of Computers,2017,40(12):2856-2870.(in Chinese) 朱利,邱媛媛,于帅,等.一种基于快速k-近邻的最小生成树离群检测方法[J].计算机学报,2017,40(12):2856-2870. [47]PENG T,YANG N Y,XU Y B,et al.An Outlier Detection Method Based on Ranking and Clustering in Bi-typed Heterogeneous Network[J].Acta Electronica Sinica,2018,46(2):281-288.(in Chinese) 彭涛,杨妮亚,徐原博,等.双类型异质网中基于排序和聚类的离群点检测方法[J].电子学报,2018,46(2):281-288. [48]LIU Y,WANG L M,JIANG J H,et al.SVM Credit Risk Eva- luation Method Based on Eliminating Outliers[J].Journal of Jilin University (Science Edition),2016,54(6):1395-1400.(in Chinese) 刘颖,王丽敏,姜建华,等.基于离群点剔除的SVM信用风险评价方法[J].吉林大学学报(理学版),2016,54(6):1395-1400. [49]KNORR E M,NG R T.Algorithms for mining distance-based outliers in large datasets[C]∥ Proceedings of the 24th International Conference on Very Large Data Bases.New York,USA,1998:392-403. [50]WANG Y,PARTHASARATHY S,TATIKONDA S.Locality sensitive outlier detection:A ranking driven approach[C]∥Proceedings of the IEEE 27th International Conference on Data Engineering.Hannover,Germany,2011:410-421. [51]PILLUTLA M R,RAVAL N,BANSAL P,et al.LSH based outlier detection and its application in distributed setting[C]∥Proceedings of the 20th ACM International Conference on Information and Knowledge Management.Glasgow,UK,2011:2289-2292. [52]WANG X T,SHEN D R,BAI M,et al.BOD:An Efficient Algorithm for Distributed Outlier Detection[J].Chinese Journal of Computers,2016,39(1):36-50.(in Chinese) 王习特,申德荣,白梅,等.BOD:一种高效的分布式离群点检测算法[J].计算机学报,2016,39(1):36-50. [53]JIANG F,SUI Y F,CAO C G.Distance metrics and outlier detection in rough sets[J].Control and Decision,2013,28(1):188-192.(in Chinese) 江峰,眭跃飞,曹存根.粗糙集中的距离度量与离群点检测[J].控制与决策,2013,28(1):188-192. [54]YAO X,YU L A.A fuzzy proximal support vector machine model and its application to credit risk analysis[J].Systems Engineering-Theory & Practice,2012,32(3):549-554.(in Chinese) 姚潇,余乐安.模糊近似支持向量机模型及其在信用风险评估中的应用[J].系统工程理论与实践,2012,32(3):549-554. [55]LIU J L,LI J P,XU W X,et al.A Robust Weighted Adaptive LpLS-SVM Method for Credit Risk Assessment[J].Chinese Journal of Management Science,2010,18(5):28-33.(in Chinese) 刘京礼,李建平,徐伟宣,等.信用评估中的鲁棒赋权自适应Lp最小二乘支持向量机方法[J].中国管理科学,2010,18(5):28-33. [56]BHADURI K,MATTHEWS B L,GIANNELLA C R.Algo- rithms for speeding up distance-based outlier detection[C]∥Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Diego,USA,2011:859-867. [57]BREUNIG M M.LOF:Identifying density-based local outliers [J].ACM Sigmod Record,2015,29(2):93-104. [58]JIN W,TUNG A K H,HAN J,et al.Ranking outliers using symmetric neighborhood relationship[J].Lecture Notes in Computer Science,2006,3918:577-593. [59]ZHOU S B,XU W X.Deviation-based local outlier detection algorithm[J].Chinese Journal of Scientific Instrument,2014,35(10):2293-2298.(in Chinese) 周世波,徐维祥.一种基于偏离的局部离群点检测算法[J].仪器仪表学报,2014,35(10):2293-2298. [60]LIU Z T,XU J P,WU M,et al.Review of Emotional Feature Extraction and Dimension Reduction Method for Speech Emotion Recognition[J/OL].Chinese Journal of Computers,http://kns.cnki.net/kcms/detail/11.1826.TP.20170813.1200.006.html.(in Chinese) 刘振焘,徐建平,吴敏,等.语音情感特征提取及其降维方法综述[J/OL].计算机学报,http://kns.cnki.net/kcms/detail/11.1826.TP.20170813.1200.006.html. [61]MENG D Y,XU C,XU Z B.A New Manifold Reconstruction Method Based on Isomap[J].Chinese Journal of Computers,2010,33(3):545-554.(in Chinese) 孟德宇,徐晨,徐宗本.基于Isomap的流形结构重建方法[J].计算机学报,2010,33(3):545-554. [62]ZHANG R C,DU Y B,XUE L G,et al.A hybrid large sample credit evaluation model based on combining similar samples[J].Journal of Management Sciences in China,2018,21(7):77-90.(in Chinese) 张润驰,杜亚斌,薛立国,等.基于相似样本归并的大样本混合信用评估模型[J].管理科学学报,2018,21(7):77-90. [63]CHEN W S,DU Y K.Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model[J].Expert Systems with Applications,2009,36:4075-4086. [64]PAN H P,ZHANG C Z.FEPA-An Adaptive Integrated Prediction Model of Financial Time Series[J].Chinese Journal of Management Science,2018,26(6):26-38.(in Chinese) 潘和平,张承钊.FEPA-金融时间序列自适应组合预测模型[J].中国管理科学,2018,26(6):26-38. [65]WEST D.Neural network credit scoring models[J].Computer &Operations Research,2000,27:1131-1152. [66]HUA Z,WANG Z,XU X,et al.Predicting Corporate Financial Distress Based on Integration of Support Vector Machine and Logistic Regression[J].Expert Systems with Applications,2007,33(2):434-440. [67]XIONG Z B.Research on Credit Evaluation Model Based on Nonlinear Principal Component Analysis[J].The Journal of Quantitative & Technical Economics,2013(10):138-151.(in Chinese) 熊志斌.基于非线性主成分分析的信用评估模型研究[J].数量经济技术经济研究,2013(10):138-151. [68]ZHANG H X,MAO Z Z.Research of multidimensional time series credit evaluation based on gray-fuzz analysis model[J].Journal of Management Sciences in China,2011,14(1):28-37.(in Chinese) 张洪祥,毛志忠.基于多维时间序列的灰色模糊信用评价研究[J].管理科学学报,2011,14(1):28-37. [69]ZHANG J,ZHANG B B.The Application of Generalized Semi-parametric Additive Credit Score Model Based on Group-LASSO Method[J].Journal of Applied Statistics and Management,2016,35(3):517-524.(in Chinese) 张娟,张贝贝.基于Group-LASSO方法的广义半参数可加信用风险评分模型应用研究[J].数理统计与管理,2016,35(3):517-524. [70]TENENBAUM J B,SILVA V,LANGFORD J C.A global geometric framework for nonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2323. [71]LI F Y,DENG X.The Application Analysis of SVM Model Based on Isomap in the Credit Risk Assessment of Listed Companies[J].Journal of Hebei University (Philosophy and Social Science),2013,38(1):102-107.(in Chinese) 李菲雅,邓翔.等距特征映射的支持向量机模型在上市公司信用风险评估中的应用[J].河北大学学报(哲学社会科学版),2013,38(1):102-107. [72]LIN F,YEH C C,LEE M Y.The use of hybrid manifold lear ning and support vector machines in the prediction of business failure[J].Knowledge-Based Systems,2011,24(1):95-101. [73]RIBEIRO B,VIEIRA A,DUARTE J,et al.Learning manifolds for bankruptcy analysis[M]∥Advances in Neuro-Information Processing—ICONIP 2008.Berlin:Springer,2008:723-730. [74]TONG G G,LI S W.Construction and Application Research of Isomap-RVM Credit Assessment Model[J].Mathematical Problems in Engineering,2015,2015:1-7. [75]XUE A R,YAO L,JU S G,et al.Survey of Outlier Mining[J].Computer Science,2008,35(11):13-18.(in Chinese) 薛安荣,姚林,鞠时光,等.离群点挖掘方法综述[J].计算机科学,2008,35(11):13-18. [76]CHEN F L,LI F C.Combination of feature selection approaches with svm in credit scoring[J].Expert System Application,2010,37:4902-4909. [77]LIU Y,ZHANG L J,HAN Y N,et al.Credit Risk Evaluation Model of Supply Chain Finance Based on Particle Swarm Coo-perative Optimization Algorithm[J].Journal of Jilin University(Science Edition),2018,56(1):119-125.(in Chinese) 刘颖,张丽娟,韩亚男,等.基于粒子群协同优化算法的供应链金融信用风险评价模型[J].吉林大学学报(理学版),2018,56(1):119-125. [78]HUANG C L,CHEN M C,WANG C J.Credit scoring with a data mining approach based on support vector machines[J].Expert System Application,2007,33:847-856. [79]WANG D,ZHANG Z Q,BAI R Q,et al.A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring[J].Journal of Computational and Applied Mathematics,2018,329:307-321. [80]HAGSTROM M.High-performance analytics fuels innovation and inclusive growth:Use big data,hyper connectivity and speed to intelligence to get true value in the digital economy[J].Journal of Advanced Analytics,2012,2:3-4. |
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