Computer Science ›› 2019, Vol. 46 ›› Issue (2): 1-10.doi: 10.11896/j.issn.1002-137X.2019.02.001
• Big Data & Data Science • Next Articles
LIU Ying
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| [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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