BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Chang Y, Chang K, Wu G. Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Applied Soft Computing 2018;73:914-20. [DOI: 10.1016/j.asoc.2018.09.029] [Cited by in Crossref: 88] [Cited by in F6Publishing: 94] [Article Influence: 22.0] [Reference Citation Analysis]
Number Citing Articles
1 Yang M, Lim MK, Qu Y, Li X, Ni D. Deep neural networks with L1 and L2 regularization for high dimensional corporate credit risk prediction. Expert Systems with Applications 2023;213:118873. [DOI: 10.1016/j.eswa.2022.118873] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Liu W, Fan H, Xia M, Xia M. A focal-aware cost-sensitive boosted tree for imbalanced credit scoring. Expert Systems with Applications 2022;208:118158. [DOI: 10.1016/j.eswa.2022.118158] [Reference Citation Analysis]
3 Zou Y, Gao C, Xia M, Pang C. Credit scoring based on a Bagging-cascading boosted decision tree. IDA 2022;26:1557-1578. [DOI: 10.3233/ida-216228] [Reference Citation Analysis]
4 Li X, Tang X, Cheng Q. Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network. Journal of Informetrics 2022;16:101333. [DOI: 10.1016/j.joi.2022.101333] [Reference Citation Analysis]
5 Luo Q, Jia Z, Li H, Wu Y. Analysis of parametric and non-parametric option pricing models. Heliyon 2022;8:e11388. [DOI: 10.1016/j.heliyon.2022.e11388] [Reference Citation Analysis]
6 Garkavenko M, Beliaeva T, Gaussier E, Mirisaee H, Lagnier C, Guerraz A. Assessing the Factors Related to a Start-Up’s Valuation Using Prediction and Causal Discovery. Entrepreneurship Theory and Practice. [DOI: 10.1177/10422587221121291] [Reference Citation Analysis]
7 Irshad MT, Nisar MA, Huang X, Hartz J, Flak O, Li F, Gouverneur P, Piet A, Oltmanns KM, Grzegorzek M. SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors. Sensors 2022;22:7711. [DOI: 10.3390/s22207711] [Reference Citation Analysis]
8 Bueff AC, Cytryński M, Calabrese R, Jones M, Roberts J, Moore J, Brown I. Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals. Expert Systems with Applications 2022;202:117271. [DOI: 10.1016/j.eswa.2022.117271] [Reference Citation Analysis]
9 Mushava J, Murray M. A novel XGBoost extension for credit scoring class-imbalanced data combining a generalized extreme value link and a modified focal loss function. Expert Systems with Applications 2022;202:117233. [DOI: 10.1016/j.eswa.2022.117233] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
10 Zhou L, Ma C. A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps. Comput Econ. [DOI: 10.1007/s10614-022-10308-9] [Reference Citation Analysis]
11 Gou Q, Niu H, Gao W, Qin B, Li J. Research on financial loan default risk prediction based on integrated model. International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022) 2022. [DOI: 10.1117/12.2642663] [Reference Citation Analysis]
12 Dhief I, Alam S, Lilith N, Mean CC. A machine learned go-around prediction model using pilot-in-the-loop simulations. Transportation Research Part C: Emerging Technologies 2022;140:103704. [DOI: 10.1016/j.trc.2022.103704] [Reference Citation Analysis]
13 de-Prado-Gil J, Palencia C, Jagadesh P, Martínez-García R. A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete. Materials (Basel) 2022;15:4164. [PMID: 35744223 DOI: 10.3390/ma15124164] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Deng S, Zhu Y, Huang X, Duan S, Fu Z. High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method. Future Internet 2022;14:180. [DOI: 10.3390/fi14060180] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Liu J, Zhang S, Fan H. A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural network. Expert Systems with Applications 2022;195:116624. [DOI: 10.1016/j.eswa.2022.116624] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
16 Dang Y, Chen Z, Li H, Shu H. A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction. Applied Artificial Intelligence 2022;36:2074129. [DOI: 10.1080/08839514.2022.2074129] [Reference Citation Analysis]
17 Szymura A. Risk Assessment of Polish Joint Stock Companies: Prediction of Penalties or Compensation Payments. Risks 2022;10:102. [DOI: 10.3390/risks10050102] [Reference Citation Analysis]
18 Twum AK, Agyemang AO, Sare YA. Revisiting credit risk and banks performance of China's commercial banks before and after Covid 19 pandemic. J Corp Accounting Finance. [DOI: 10.1002/jcaf.22539] [Reference Citation Analysis]
19 Guerra P, Castelli M, Côrte-real N. Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System. Risks 2022;10:71. [DOI: 10.3390/risks10040071] [Reference Citation Analysis]
20 Wang J, Rong W, Zhang Z, Mei D, Qureshi NMF. Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China. Wireless Communications and Mobile Computing 2022;2022:1-14. [DOI: 10.1155/2022/8005493] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Ergen B, Sertkaya ME. Alzheimer Hastalığının Erken Teşhisinin Çoklu Değişken Kullanarak Tespiti. European Journal of Science and Technology 2022. [DOI: 10.31590/ejosat.1082297] [Reference Citation Analysis]
22 Consoli S, Tiozzo Pezzoli L, Tosetti E. Neural forecasting of the Italian sovereign bond market with economic news. Royal Stats Society Series A. [DOI: 10.1111/rssa.12813] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 He N, Yongqiao W, Tao J, Zhaoyu C. Self-Adaptive bagging approach to credit rating. Technological Forecasting and Social Change 2022;175:121371. [DOI: 10.1016/j.techfore.2021.121371] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Gumaei A, Ismail WN, Rafiul Hassan M, Hassan MM, Mohamed E, Alelaiwi A, Fortino G. A Decision-Level Fusion Method for COVID-19 Patient Health Prediction. Big Data Research 2022;27:100287. [DOI: 10.1016/j.bdr.2021.100287] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
25 Ben Jabeur S, Stef N, Carmona P. Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering. Comput Econ. [DOI: 10.1007/s10614-021-10227-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
26 Lin H, Zhao Y. Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China. Front Ecol Evol 2022;9:771439. [DOI: 10.3389/fevo.2021.771439] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Shen F, Zhang X, Wang R, Lan D, Zhou W. Sequential optimization three-way decision model with information gain for credit default risk evaluation. International Journal of Forecasting 2022. [DOI: 10.1016/j.ijforecast.2021.12.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
28 Mankinen T, Stoklasa J, Luukka P. Predicting Short-Term Traffic Speed and Speed Drops in the Urban Area of a Medium-Sized European City—A Traffic Control and Decision Support Perspective. Intelligent Systems and Applications in Business and Finance 2022. [DOI: 10.1007/978-3-030-93699-0_7] [Reference Citation Analysis]
29 Cui S, Qiu H, Wang S, Wang Y. Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction. Applied Soft Computing 2021;113:107989. [DOI: 10.1016/j.asoc.2021.107989] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Datar PV, Kulkarni DB. A XGBOOST‐MRFO control scheme for power quality improvement in grid integrated hybrid renewable energy sources using STATCOM. Int Trans Electr Energ Syst 2021;31. [DOI: 10.1002/2050-7038.13181] [Reference Citation Analysis]
31 Wu Y, Tong G. The evaluation of agricultural enterprise's innovative borrowing capacity based on deep learning and BP neural network. Int J Syst Assur Eng Manag 2021. [DOI: 10.1007/s13198-021-01462-8] [Reference Citation Analysis]
32 Xiao Z, Jiao J. Interpretable Credit Risk Assessment Based on Heuristic Knowledge Extraction Method. 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) 2021. [DOI: 10.1109/ictai52525.2021.00196] [Reference Citation Analysis]
33 Zhou L, Fujita H, Ding H, Ma R. Credit risk modeling on data with two timestamps in peer-to-peer lending by gradient boosting. Applied Soft Computing 2021;110:107672. [DOI: 10.1016/j.asoc.2021.107672] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
34 Deng S, Huang X, Qin Z, Fu Z, Yang T. A novel hybrid method for direction forecasting and trading of Apple Futures. Applied Soft Computing 2021;110:107734. [DOI: 10.1016/j.asoc.2021.107734] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
35 Castillo JA, Mora-valencia A, Perote J. Moral hazard index for credit risk to SMEs. International Economics 2021. [DOI: 10.1016/j.inteco.2021.10.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Androulakis E, Galanis G. A two-step hybrid system towards optimized wave height forecasts. Stoch Environ Res Risk Assess 2022;36:753-66. [DOI: 10.1007/s00477-021-02075-0] [Reference Citation Analysis]
37 Yıldırım M, Okay FY, Özdemir S. Big data analytics for default prediction using graph theory. Expert Systems with Applications 2021;176:114840. [DOI: 10.1016/j.eswa.2021.114840] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
38 Guerra P, Castelli M. Machine Learning Applied to Banking Supervision a Literature Review. Risks 2021;9:136. [DOI: 10.3390/risks9070136] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
39 Cui S, Wang Y, Yin Y, Cheng T, Wang D, Zhai M. A cluster-based intelligence ensemble learning method for classification problems. Information Sciences 2021;560:386-409. [DOI: 10.1016/j.ins.2021.01.061] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 11.0] [Reference Citation Analysis]
40 Xiong Z, Huang J. Prediction of credit risk with an ensemble model: a correlation-based classifier selection approach. JM2 2021;ahead-of-print. [DOI: 10.1108/jm2-09-2020-0235] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Jabeur SB, Gharib C, Mefteh-wali S, Arfi WB. CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change 2021;166:120658. [DOI: 10.1016/j.techfore.2021.120658] [Cited by in Crossref: 25] [Cited by in F6Publishing: 19] [Article Influence: 25.0] [Reference Citation Analysis]
42 Alaminos D, Peláez JI, Salas MB, Fernández-gámez MA. Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques. Symmetry 2021;13:652. [DOI: 10.3390/sym13040652] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
43 Jiao W, Hao X, Qin C. The Image Classification Method with CNN-XGBoost Model Based on Adaptive Particle Swarm Optimization. Information 2021;12:156. [DOI: 10.3390/info12040156] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
44 Wang N, Zhao S, Cui S, Fan W. A hybrid ensemble learning method for the identification of gang-related arson cases. Knowledge-Based Systems 2021;218:106875. [DOI: 10.1016/j.knosys.2021.106875] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
45 Qin C, Zhang Y, Bao F, Zhang C, Liu P, Liu P, Kotsiantis SB. XGBoost Optimized by Adaptive Particle Swarm Optimization for Credit Scoring. Mathematical Problems in Engineering 2021;2021:1-18. [DOI: 10.1155/2021/6655510] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 12.0] [Reference Citation Analysis]
46 Park S, Kim J. The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential. Sustainability 2021;13:2459. [DOI: 10.3390/su13052459] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
47 Arabameri A, Chandra Pal S, Costache R, Saha A, Rezaie F, Seyed Danesh A, Pradhan B, Lee S, Hoang N. Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics, Natural Hazards and Risk 2021;12:469-98. [DOI: 10.1080/19475705.2021.1880977] [Cited by in Crossref: 25] [Cited by in F6Publishing: 20] [Article Influence: 25.0] [Reference Citation Analysis]
48 Zhou Y, Uddin MS, Habib T, Chi G, Yuan K. Feature selection in credit risk modeling: an international evidence. Economic Research-Ekonomska Istraživanja 2021;34:3064-91. [DOI: 10.1080/1331677x.2020.1867213] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
49 Saito M, Ohsato T, Yamanaka S. An empirical evaluation of machine learning performance in corporate sales growth prediction. JSIAM Letters 2021;13:25-8. [DOI: 10.14495/jsiaml.13.25] [Reference Citation Analysis]
50 Liu S, Wei J, Chen X, Wang C, Wang XA. Credit Rating Based on Hybrid Sampling and Dynamic Ensemble. Advances in Intelligent Networking and Collaborative Systems 2021. [DOI: 10.1007/978-3-030-57796-4_33] [Reference Citation Analysis]
51 Zhang Y, Chen L. A Study on Forecasting the Default Risk of Bond Based on XGboost Algorithm and Over-Sampling Method. TEL 2021;11:258-67. [DOI: 10.4236/tel.2021.112019] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
52 Deng S, Huang X, Wang J, Qin Z, Fu Z, Wang A, Yang T. A Decision Support System for Trading in Apple Futures Market Using Predictions Fusion. IEEE Access 2021;9:1271-85. [DOI: 10.1109/access.2020.3047138] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
53 Liu W, Fan H, Xia M. Step-wise multi-grained augmented gradient boosting decision trees for credit scoring. Engineering Applications of Artificial Intelligence 2021;97:104036. [DOI: 10.1016/j.engappai.2020.104036] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 13.0] [Reference Citation Analysis]
54 Zoppi T, Ceccarelli A. Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring. IEEE Access 2021;9:150579-150591. [DOI: 10.1109/access.2021.3125920] [Reference Citation Analysis]
55 Ivașcu C. Option pricing using Machine Learning. Expert Systems with Applications 2021;163:113799. [DOI: 10.1016/j.eswa.2020.113799] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 8.0] [Reference Citation Analysis]
56 Consoli S, Tiozzo Pezzoli L, Tosetti E. Information Extraction From the GDELT Database to Analyse EU Sovereign Bond Markets. Mining Data for Financial Applications 2021. [DOI: 10.1007/978-3-030-66981-2_5] [Reference Citation Analysis]
57 Kim H, Lee SJ, Park SJ, Choi IY, Hong S. Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study (Preprint).. [DOI: 10.2196/preprints.25635] [Reference Citation Analysis]
58 Marani A, Nehdi ML. Machine learning prediction of compressive strength for phase change materials integrated cementitious composites. Construction and Building Materials 2020;265:120286. [DOI: 10.1016/j.conbuildmat.2020.120286] [Cited by in Crossref: 48] [Cited by in F6Publishing: 51] [Article Influence: 24.0] [Reference Citation Analysis]
59 Fan S, Shen Y, Peng S, Xia M. Improved ML-Based Technique for Credit Card Scoring in Internet Financial Risk Control. Complexity 2020;2020:1-14. [DOI: 10.1155/2020/8706285] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
60 Hou W, Wang X, Zhang H, Wang J, Li L. A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment. Knowledge-Based Systems 2020;208:106462. [DOI: 10.1016/j.knosys.2020.106462] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 10.0] [Reference Citation Analysis]
61 Zhang M, Gong H, Jia X, Xiao R, Jiang X, Ma Y, Huang B. Analysis of critical factors to asphalt overlay performance using gradient boosted models. Construction and Building Materials 2020;262:120083. [DOI: 10.1016/j.conbuildmat.2020.120083] [Cited by in Crossref: 9] [Cited by in F6Publishing: 13] [Article Influence: 4.5] [Reference Citation Analysis]
62 Blohm I, Antretter T, Sirén C, Grichnik D, Wincent J. It’s a Peoples Game, Isn’t It?! A Comparison Between the Investment Returns of Business Angels and Machine Learning Algorithms. Entrepreneurship Theory and Practice. [DOI: 10.1177/1042258720945206] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
63 Arjasakusuma S, Swahyu Kusuma S, Phinn S. Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data. IJGI 2020;9:507. [DOI: 10.3390/ijgi9090507] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
64 Gholami H, Mohamadifar A, Sorooshian A, Jansen JD. Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran. Atmospheric Pollution Research 2020;11:1303-15. [DOI: 10.1016/j.apr.2020.05.009] [Cited by in Crossref: 37] [Cited by in F6Publishing: 41] [Article Influence: 18.5] [Reference Citation Analysis]
65 Wang C, Deng C, Wang S. Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost. Pattern Recognition Letters 2020;136:190-7. [DOI: 10.1016/j.patrec.2020.05.035] [Cited by in Crossref: 59] [Cited by in F6Publishing: 63] [Article Influence: 29.5] [Reference Citation Analysis]
66 Ilhan HO, Serbes G, Aydin N. Automated sperm morphology analysis approach using a directional masking technique. Computers in Biology and Medicine 2020;122:103845. [DOI: 10.1016/j.compbiomed.2020.103845] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
67 Dastile X, Celik T, Potsane M. Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing 2020;91:106263. [DOI: 10.1016/j.asoc.2020.106263] [Cited by in Crossref: 75] [Cited by in F6Publishing: 1] [Article Influence: 37.5] [Reference Citation Analysis]
68 Wang L, Chen Y, Jiang H, Yao J. Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble. Applied Soft Computing 2020;91:106262. [DOI: 10.1016/j.asoc.2020.106262] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
69 Dastile X, Celik T, Potsane M. Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing 2020;91:106263. [DOI: 10.1016/j.asoc.2020.106263] [Cited by in Crossref: 66] [Cited by in F6Publishing: 39] [Article Influence: 33.0] [Reference Citation Analysis]
70 Zhang J, Feng Q, Zhang X, Shu C, Wang S, Wu K. A Supervised Learning Approach for Accurate Modeling of CO 2 –Brine Interfacial Tension with Application in Identifying the Optimum Sequestration Depth in Saline Aquifers. Energy Fuels 2020;34:7353-62. [DOI: 10.1021/acs.energyfuels.0c00846] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 7.0] [Reference Citation Analysis]
71 Putra YK, Fathurrahman, Sadali M. Comparison Of Pso-Based Naive Bayes And Naive Bayes Algorithm In Determining The Feasibility Of Bumdes Credit. J Phys : Conf Ser 2020;1539:012030. [DOI: 10.1088/1742-6596/1539/1/012030] [Reference Citation Analysis]
72 Gholami H, Mohamadifar A, Collins AL. Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling. Atmospheric Research 2020;233:104716. [DOI: 10.1016/j.atmosres.2019.104716] [Cited by in Crossref: 43] [Cited by in F6Publishing: 36] [Article Influence: 21.5] [Reference Citation Analysis]
73 Xiao J, Zhou X, Zhong Y, Xie L, Gu X, Liu D. Cost-sensitive semi-supervised selective ensemble model for customer credit scoring. Knowledge-Based Systems 2020;189:105118. [DOI: 10.1016/j.knosys.2019.105118] [Cited by in Crossref: 25] [Cited by in F6Publishing: 17] [Article Influence: 12.5] [Reference Citation Analysis]
74 Consoli S, Pezzoli LT, Tosetti E. Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market. Machine Learning, Optimization, and Data Science 2020. [DOI: 10.1007/978-3-030-64583-0_18] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
75 Zhang Z, Niu K, Liu Y. A Deep Learning Based Online Credit Scoring Model for P2P Lending. IEEE Access 2020;8:177307-17. [DOI: 10.1109/access.2020.3027337] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
76 Kuo K, Talley PC, Kuzuya M, Huang C. Development of a clinical support system for identifying social frailty. International Journal of Medical Informatics 2019;132:103979. [DOI: 10.1016/j.ijmedinf.2019.103979] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
77 Zhou J, Li W, Wang J, Ding S, Xia C. Default prediction in P2P lending from high-dimensional data based on machine learning. Physica A: Statistical Mechanics and its Applications 2019;534:122370. [DOI: 10.1016/j.physa.2019.122370] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 7.0] [Reference Citation Analysis]
78 Asante-okyere S, Shen C, Ziggah YY, Rulegeya MM, Zhu X. A Novel Hybrid Technique of Integrating Gradient-Boosted Machine and Clustering Algorithms for Lithology Classification. Nat Resour Res 2020;29:2257-73. [DOI: 10.1007/s11053-019-09576-4] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
79 de Castro Vieira JR, Barboza F, Sobreiro VA, Kimura H. Machine learning models for credit analysis improvements: Predicting low-income families’ default. Applied Soft Computing 2019;83:105640. [DOI: 10.1016/j.asoc.2019.105640] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 4.7] [Reference Citation Analysis]
80 Mustika WF, Murfi H, Widyaningsih Y. Analysis Accuracy of XGBoost Model for Multiclass Classification - A Case Study of Applicant Level Risk Prediction for Life Insurance. 2019 5th International Conference on Science in Information Technology (ICSITech) 2019. [DOI: 10.1109/icsitech46713.2019.8987474] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
81 Le T, Vo B, Fujita H, Nguyen N, Baik SW. A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting. Information Sciences 2019;494:294-310. [DOI: 10.1016/j.ins.2019.04.060] [Cited by in Crossref: 29] [Cited by in F6Publishing: 32] [Article Influence: 9.7] [Reference Citation Analysis]
82 Kanapickiene R, Spicas R. Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania. Risks 2019;7:67. [DOI: 10.3390/risks7020067] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]