[1] |
Wang D, Huang Y, Cai Z. The State Council Information Office held a press conference on tax and fee reduction to boost confidence in the development [Internet]. Beijing: The State Council Information Office of the People’s Republic of China; 2022 Jan 26 [cited 2022 Nov 1]. Available from: http://www.scio.gov.cn/xwfbh/xwbfbh/wqfbh/47673/47802/index.htmChinese.
|
[2] |
The tax gap—tax gap estimates for tax years 2014-2016 [Internet]. Washington, DC: Internal Revenue Service; 2022 Oct 28 [cited 2022 Nov 1]. Available from: https://www.irs.gov/newsroom/the-tax-gap
|
[3] |
A. Androniceanu, R. Gherghina, M. Ciobănaşu. The interdependence between fiscal public policies and tax evasion. Adm Si Manag Public, 32 (2019), pp. 32-41.
|
[4] |
J.J. López. A quantitative theory of tax evasion. J Macroecon, 53 (2017), pp. 107-126.
|
[5] |
M.G. Allingham, A. Sandmo. Income tax evasion: a theoretical analysis. J Public Econ, 1 (3-4) (1972), pp. 323-338.
|
[6] |
Zhao Q, Bhowmick SS. Association rule mining:a survey. Report. Singapore: Nanyang Technological University; 2003.
|
[7] |
J. Hipp, U. Güntzer, G. Nakhaeizadeh. Algorithms for association rule mining—a general survey and comparison. SIGKDD Explor, 2 (1) (2000), pp. 58-64.
|
[8] |
R.S. Wu, C.S. Ou, H. Lin, S.I. Chang, D.C. Yen. Using data mining technique to enhance tax evasion detection performance. Expert Syst Appl, 39 (10) (2012), pp. 8769-8777.
|
[9] |
Matos T, Monteiro JM. An empirical method for discovering tax fraudsters:a real case study of Brazilian fiscal evasion. In: Proceedings of the 19th International Database Engineering & Applications Symposium; 2015 Jul 13-15; Yokohama, Japan. New York City: Association for Computing Machinery (ACM); 41-8.
|
[10] |
Z. Zhao, Z. Jian, G.S. Gaba, R. Alroobaea, M. Masud, S. Rubaiee. An improved association rule mining algorithm for large data. J Intell Syst, 30 (1) (2021), pp. 750-762.
|
[11] |
S.R. Safavian, D. Landgrebe. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern, 21 (3) (1991), pp. 660-674.
|
[12] |
L.A. Clark, D. Pregibon. Tree-based models. T.J. Hastie (Ed.), Statistical models in S, Taylor & Francis Group, New York City (2017).
|
[13] |
Bonchi F, Giannotti F, Mainetto G, Pedreschi D. Using data mining techniques in fiscal fraud detection. In:Proceedings of the 1st International Conference on Data Warehousing and Knowledge Discovery; 1999 Aug 30-Sep 1; Florence, Italy. Berlin:Springer; 1999. p. 369-76.
|
[14] |
Mittal S, Reich O, Mahajan A. Who is bogus? Using one-sided labels to identify fraudulent firms from tax returns. In: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies; 2018 Jun 20-22; Menlo Park and San Jose, CA, USA. New York City: Association for Computing Machinery (ACM); 2018. p. 1-11.
|
[15] |
Yao J, Zhang J, Wang L. A financial statement fraud detection model based on hybrid data mining methods. In: Proceedings of the 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD); 2018 May 26-28; Chengdu, China. New York City: IEEE; 57-61.
|
[16] |
C. Wu, J. Luo. Automatic recognition of tax evasion behavior based on random forest. Software Guide, 017 (008) (2018), pp. 13-16.
|
[17] |
B. An, Y. Suh. Identifying financial statement fraud with decision rules obtained from modified random forest. Data Technol Appl, 54 (2) (2020), pp. 235-255.
|
[18] |
Y.L. Ji, W.Q. Wang. The stock of research on accurate identification of tax risk under the background of big data technology—based on machine learning. Public Finance Res, 451 (09) (2020), pp. 121-131.Chinese.
|
[19] |
Andrade JPA, Paulucio LS, Paixao TM, Paixao TM, Berriel RF, Carneiro TCJ, et al. A machine learning-based system for financial fraud detection. In: Proceedings of the 18th National Meeting on Artificial and Computational Intelligence (ENIAC 2021); 2021 Nov 29-Dec 3; online. São Leopoldo: Sociedade Brasileira de Computação (SBC); 2021. p. 165-76.
|
[20] |
O.C. Xavier, S.R. Pires, T.C. Marques, A.S. Soares. Tax evasion identification using open data and artificial intelligence. Rev Adm Pública, 56 (3) (2022), pp. 426-440.
|
[21] |
Agarwal A, Tan YS, Ronen O, Singh C, Yu B. Hierarchical Shrinkage:improving the accuracy and interpretability of tree-based models. In: Proceedings of the 39th International Conference on Machine Learning; 2022 Jul 17-23; Baltimore, MD, USA. New York City: ML Research Press; 2022. p. 111-35.
|
[22] |
W.S. Noble. What is a support vector machine?. Nat Biotechnol, 24 (12) (2006), pp. 1565-1567.
|
[23] |
D.A. Pisner, D.M. Schnyer. Support vector machine. A. Mechelli, S. Vieira (Eds.), Machine learning, Academic Press, Cambridge (2020), pp. 101-121.
|
[24] |
S. Wang, A. Li. Fraud detection in tax declaration based on SVM. Comput Eng (2006;).
|
[25] |
H. Liu, X. Yu, W. Wan, X. Ma. A tax assessment model based on rough set theory and SVM algorithms. Comput Simu, 26 (12) (2009), pp. 253-256.Chinese.
|
[26] |
H. Xia, R. Li. Cases-choice in tax declaration model based on SVM and SOM. Sci Technol Eng, 009 (014) (2009), pp. 4027-4031.Chinese.
|
[27] |
Junqué de Fortuny E, Stankova M, Moeyersoms J, Minnaert B, Provost FJ, Martens D. Corporate residence fraud detection. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2014 Aug 24-27; New York City, NY, USA. New York City: Association for Computing Machinery (ACM); 2014. p. 1650-9.
|
[28] |
Rad MS, Shahbahrami A. Detecting high risk taxpayers using data mining techniques. In: Proceedings of the 2016 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS 2016); 2016 Dec 14-15; Tehran, Iran. New York City: IEEE; 1-5.
|
[29] |
X. Zhang. Early warning and investigation countermeasures of crime of issuing false invoice [dissertation]. People’s Public Security University of China, Beijing (2020).Chinese.
|
[30] |
J. Cervantes, F. Garcia-Lamont, L. Rodriguez, A. López, J.R. Castilla, A. Trueba. PSO-based method for SVM classification on skewed data sets. Neurocomputing, 228 (2017), pp. 187-197.
|
[31] |
Rish I. An empirical study of the Naive Bayes classifier. In:Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence; 2001 Aug 4-6; Washington, DC, USA. Berlin:Springer; 2001. p. 41-6.
|
[32] |
K.M. Leung. Naive Bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, Hong Kong (2007).
|
[33] |
E. Kirkos, C. Spathis, Y. Manolopoulos. Data mining techniques for the detection of fraudulent financial statements. Expert Syst Appl, 32 (4) (2007), pp. 995-1003.
|
[34] |
Z. Kang, Y. Yu. Study on tax evaluation model based Bayesian classification. Econ Probl, 6 (2009), pp. 124-126.Chinese.
|
[35] |
K. Zhang, D. Wu, A. Li, B.W. Song. Fraud detection in tax declaration based on Bayesian classifier. Comput Simu, 27 (009) (2010), pp. 306-310.Chinese.
|
[36] |
Lenz HJ. Tax fraud and investigation procedures-everybody, every where, every time. In:Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP 2016); 2016 Feb 19-21; Rome, Italy. Trier: The DBLP Computer Science Bibliography; 2016. p. 3-13.
|
[37] |
N.A. Zaidi, J. Cerquides, M.J. Carman, G.I. Webb. Alleviating Naive Bayes attribute independence assumption by attribute weighting. J Mach Learn Res, 14 (60) (2013), pp. 1947-1988.
|
[38] |
D.G. Kleinbaum, M. Klein. Logistic regression: a self-learning text. (2nd ed.), Springer-Verlag, New York City (2002).
|
[39] |
D.W. Hosmer Jr, S. Lemeshow, R.X. Sturdivant. Applied logistic regression. John Wiley & Sons, Hoboken (2013).
|
[40] |
X. Qi. The research on the tax inspection methods about identifying tax evasion [dissertation]. Jilin University, Changchun (2010).Chinese.
|
[41] |
Y. Wang, Q. Li, X. Qi. Research on the tax inspection selection scheme model based on the logistic regression. Econ Res Guide, 35 (2) (2012), pp. 96-97.Chinese.
|
[42] |
Y. Su. Research on tax inspection case selection based on logistic regression model [dissertation]. Sun Yat-sen University, Guangzhou (2011).Chinese.
|
[43] |
Y. Yuan. Research on the model of tax inspection and case selection in H city based on logistic regression to identify enterprise tax evasion [dissertation]. Inner Mongolia University, Hohhot (2019).Chinese.
|
[44] |
J. Lever, M. Krzywinski, N. Altman. Points of significance: model selection and overfitting. Nat Methods, 13 (9) (2016), pp. 703-705.
|
[45] |
Frades I, Matthiesen R. Overview on techniques in cluster analysis. In: Matthiesen R, editor. Bioinformatics methods in clinical research. Totowa: Humana Press; 2010.
|
[46] |
B.S. Duran, P.L. Odell. Cluster analysis:a survey. Springer Science & Business Media, Berlin (2013).
|
[47] |
Denny, Williams GJ, Christen P. Exploratory multilevel hot spot analysis:Australian taxation office case study. In: Proceedings of the 6th Australasian Conference on Data Mining and Analytics-Volume 70; 2007 Dec 3-4; Queensland, QLD, Australia. New York City: Association for Computing Machinery (ACM); 2007. p. 77-84.
|
[48] |
Liu X, Pan D, Chen S. Application of hierarchical clustering in tax inspection case-selecting. In: Proceedings of the 2010 International Conference on Computational Intelligence and Software Engineering; 2010 Dec 10-12; Wuhan, China. New York City: IEEE; 1-4.
|
[49] |
B. Liu, G. Xu, Q. Xu, N. Zhang. Outlier detection data mining of tax based on cluster. Phys Procedia, 33 (2012), pp. 1689-1694.
|
[50] |
Assylbekov Z, Melnykov I, Bekishev R, Baltabayeva A, Bissengaliyeva D, Mamlin E. Detecting value-added tax evasion by business entities of Kazakhstan. In: Czarnowski I, Caballero A, Howlett R, Jain L, editors. Proceedings of the International Conference on Intelligent Decision Technologies; 2016 Jun 15-17; Puerto de la Cruz, Spain. Berlin: Springer, Cham; 2016. p. 37-49.
|
[51] |
D. De Roux, B. Perez, A. Moreno, V.M. del Pilar, C. Figueroa. Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; 2018 Aug 19-23; London, UK, Association for Computing Machinery (ACM), New York City (2018), pp. 215-222.
|
[52] |
H. Xia, P. Cheng, L. Zhang. Tax risk identification based on improved K-means clustering algorithm under big data. Fin Accou Mon, 21 (2019), pp. 143-146.Chinese.
|
[53] |
S. Ben-David, N. Haghtalab. Clustering in the presence of background noise. Proceedings of the International Conference on Machine Learning; 2014 Jun 21-26; Beijing, China, ML Research Press, New York City (2014), pp. 280-288.
|
[54] |
X. Guo, S. Li. Distributed k-clustering for data with heavy noise. Proceedings of the 32nd International Conference on Neural Information Processing Systems; 2018 Dec 3-8; Montréal, QC, Canada, Association for Computing Machinery (ACM), New York City (2018), pp. 7849-7857.
|
[55] |
C.M. Bishop. Neural networks and their applications. Rev Sci Instrum, 65 (6) (1994), pp. 1803-1832.
|
[56] |
S. Khan, H. Rahmani, S.A.A. Shah, M. Bennamoun. A guide to convolutional neural networks for computer vision. Springer, Berlin (2018).
|
[57] |
A. Sinkov, G. Asyaev, A. Mursalimov, K. Nikolskaya. Neural networks in data mining. Proceedings of the 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM); 2016 May 19-20;Chelyabinsk, Russia, IEEE, New York City (2016), pp. 1-5.
|
[58] |
J. Zhang, C. Zong. Deep neural networks in machine translation: an overview. IEEE Intell Syst, 30 (5) (2015), pp. 16-25.
|
[59] |
O.I. Abiodun, A. Jantan, A.E. Omolara, K.V. Dada, N.A. Mohamed, H. Arshad. State-of-the-art in artificial neural network applications: a survey. Heliyon, 4 (11) (2018), e00938.
|
[60] |
S. Li, X. Xiao. Application of tax payment evaluation based on fuzzy neural network. Comput Simu, 29 (01) (2012), pp. 352-355.
|
[61] |
C.C. Lin, A.A. Chiu, S.Y. Huang, D.C. Yen. Detecting the financial statement fraud: the analysis of the differences between data mining techniques and experts’ judgments. Knowl Base Syst, 89 (2015), pp. 459-470.
|
[62] |
Ioana-Florina C, Mare C. The utility of neural model in predicting tax avoidance behavior. In: Czarnowski I, Howlett RJ, Jain LC, editors. Intelligent decision technologies: proceedings of the 13th KES-IDT 2021 conference. Singapore: Springer; 2021. p. 71-81.
|
[63] |
C. Pérez López, M.J. Delgado Rodríguez. Santos S. de Lucas. Tax fraud detection through neural networks: an application using a sample of personal income taxpayers. Future Internet, 11 (4) (2019), 86.
|
[64] |
Zhang L, Nan X, Huang E, Liu S. Detecting transaction-based tax evasion activities on social media platforms using multi-modal deep neural networks. 2020. arXiv:2007.13525.
|
[65] |
Chen H, Gong L, Cheng L, You Z. Tax risk assessment model of large enterprises based on multilayer perceptron. Appl Res Comput 2020 ;37(S2):41-3+6. Chinese.
|
[66] |
L. Zhang, X. Nan, E. Huang, S. Liu. Social e-commerce tax evasion detection using multi-modal deep neural networks. Proceedings of the 2021 Digital Image Computing: Techniques and Applications (DICTA); 2021 Nov 29-Dec 1; Gold Coast, QLD, Australia, IEEE, New York City (2021), pp. 1-6.
|
[67] |
B.F. Murorunkwere, O. Tuyishimire, D. Haughton, J. Nzabanita. Fraud detection using neural networks: a case study of income tax. Future Internet, 14 (6) (2022), 168.
|
[68] |
H. Mojahedi, A. Babazadeh Sangar, M. Masdari. Towards tax evasion detection using improved particle swarm optimization algorithm. Math Probl Eng, 2022 (2022), 1027518.
|
[69] |
Alsadhan NA. Value-added tax fraud detection and anomaly feature selection using sectorial autoencoders. In: Proceedings of the Data Analytics and Management (ICDAM 2022); 2022 Jun 25-26; Jelenia Góra, Poland. Singapore: Springer; 323-31.
|
[70] |
F.L. Fan, J. Xiong, M. Li, G. Wang. On interpretability of artificial neural networks: a survey. IEEE Trans Radiat Plasma Med Sci, 5 (6) (2021), pp. 741-760.
|
[71] |
K. Kar, S. Kornblith, E. Fedorenko. Interpretability of artificial neural network models in artificial intelligence versus neuroscience. Nat Mach Intell, 4 (12) (2022), pp. 1-3.
|
[72] |
M. Buda, A. Maki, M.A. Mazurowski. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw, 106 (2018), pp. 249-259.
|
[73] |
E. Trentin, M. Gori. A survey of hybrid ANN/HMM models for automatic speech recognition. Neurocomputing, 37 (1-4) (2001), pp. 91-126.
|
[74] |
P. Ravisankar, V. Ravi, G.R. Rao, I. Bose. Detection of financial statement fraud and feature selection using data mining techniques. Decis Support Syst, 50 (2) (2011), pp. 491-500.
|
[75] |
M. Zheng. Research on tax data mining based on SAS system [dissertation]. Zhengzhou University, Zhengzhou (2012).Chinese.
|
[76] |
P.C. González, J.D. Velásquez. Characterization and detection of taxpayers with false invoices using data mining techniques. Expert Syst Appl, 40 (5) (2013), pp. 1427-1436.
|
[77] |
X.P. Song, Z.H. Hu, J.G. Du, Z.H. Sheng. Application of machine learning methods to risk assessment of financial statement fraud: evidence from China. J Forecast, 33 (8) (2014), pp. 611-626.
|
[78] |
E. Rahimikia, S. Mohammadi, T. Rahmani, M. Ghazanfari. Detecting corporate tax evasion using a hybrid intelligent system: a case study of Iran. Int J Account Inf Syst, 25 (2017), pp. 1-17.
|
[79] |
Y. Wu, Q. Zheng, Y. Gao, B. Dong, R. Wei, F. Zhang, et al. TEDM-PU: a tax evasion detection method based on positive and unlabeled learning. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data); 2019 Dec 9-12; Los Angeles, CA, USA, IEEE, New York City (2019), pp. 1681-1686.
|
[80] |
K.A. Javadian, A.S.A.A. Poor, S.M. Hosseini. A model for identification tax fraud based on improved ID3 decision tree algorithm and multilayer perceptron neural network. Manag Account, 13 (46) (2020), pp. 53-70.
|
[81] |
R.A. Rahman, S. Masrom, N. Omar, M. Zakaria. An application of machine learning on corporate tax avoidance detection model. IAES Int J Artif Intell, 9 (4) (2020), 721.
|
[82] |
E. Mekonnen. Data mining for detection of tax evasion: the case of tax payers in Addis Ababa [dissertation]. St. Mary’s University, London (2021).
|
[83] |
M. Savić, J. Atanasijević, D. Jakovetić, N. Krejić. Tax evasion risk management using a hybrid unsupervised outlier detection method. Expert Syst Appl, 193 (2022), 116409.
|
[84] |
V. Baghdasaryan, H. Davtyan, A. Sarikyan, Z. Navasardyan. Improving tax audit efficiency using machine learning: the role of taxpayer’s network data in fraud detection. Appl Artif Intell, 36 (1) (2022), 2012002.
|
[85] |
R. Schunck. Within and between estimates in random-effects models: advantages and drawbacks of correlated random effects and hybrid models. Stata J, 13 (1) (2013), pp. 65-76.
|
[86] |
X. Zhu, Z. Yan, J. Ruan, Q. Zheng, B. Dong. IRTED-TL: an inter-region tax evasion detection method based on transfer learning. Proceedings of the 2018 17th IEEE International Conference On Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE); 2018 Aug 1-3; New York City, NY, USA, IEEE, New York City (2018), pp. 1224-1235.
|
[87] |
R. Wei, B. Dong, Q. Zheng, X. Zhu, J. Ruan, H. He. Unsupervised conditional adversarial networks for tax evasion detection. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data) 2019 Dec 9-12;Los Angeles, CA, USA, IEEE, New York City (2019), pp. 1675-1680.
|
[88] |
F. Zhang, B. Shi, B. Dong, Q. Zheng, X. Ji. TTED-PU: a transferable tax evasion detection method based on positive and unlabeled learning. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC); 2020 Jul 13-17;Madrid, Spain, IEEE, New York City (2020), pp. 207-216.
|
[89] |
J. Wang, Y. Chen. Safe and robust transfer learning. Springer, Singapore (2022)
|
[90] |
J. Nam, S.J. Pan, S. Kim. Transfer defect learning. Proceedings of the 2013 35th International Conference on Software Engineering (ICSE); 2013 May 18-26;San Francisco, CA, USA, IEEE, New York City (2013), pp. 382-391
|
[91] |
Li Y. Deep reinforcement learning: an overview. 2017. arXiv:1701.07274.
|
[92] |
R.S. Sutton, A.G. Barto. Reinforcement learning:an introduction. MIT Press, Cambridge (2018).
|
[93] |
V. François-Lavet, P. Henderson, R. Islam, M.G. Bellemare, J. Pineau. An introduction to deep reinforcement learning. Found Trends Mach Learn, 11 (2018), pp. 219-354.
|
[94] |
N. Abe, P. Melville, C. Pendus, C. Reddy, D. Jensen, V. Thomas. Optimizing debt collections using constrained reinforcement learning. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2010 Jul 25-28;Washington, DC, USA, Association for Computing Machinery (ACM), New York City (2010), pp. 75-84.
|
[95] |
N.D. Goumagias, D. Hristu-Varsakelis, Y.M. Assael. Using deep Q-learning to understand the tax evasion behavior of risk-averse firms. Expert Syst Appl, 101 (2018), pp. 258-270.
|
[96] |
Bonnet C, Caron P, Barrett T, Davies I, Laterre A. One step at a time: pros and cons of multi-step meta-gradient reinforcement learning. 2021. arXiv:2111.00206.
|
[97] |
A. Jitani, A. Mahajan, Z. Zhu, H. Abou-Zeid, E.T. Fapi, H. Purmehdi. Structure-aware reinforcement learning for node-overload protection in mobile edge computing. IEEE Trans Cogn Commun Netw, 8 (4) (2022), pp. 1881-1897.
|
[98] |
T. Bäck, H.P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evol Comput, 1 (1) (1993), pp. 1-23.
|
[99] |
T. Bartz-Beielstein, J. Branke, J. Mehnen, O. Mersmann. Evolutionary algorithms. Wiley Interdiscip Rev Data Min Knowl Discov, 4 (3) (2014), pp. 178-195.
|
[100] |
M.E. Alden, D.M. Bryan, B.J. Lessley, A. Tripathy. Detection of financial statement fraud using evolutionary algorithms. J Emerg Technol Account, 9 (1) (2012), pp. 71-94.
|
[101] |
G. Warner, S. Wijesinghe, U. Marques, O. Badar, J. Rosen, E. Hemberg, et al. Modeling tax evasion with genetic algorithms. Econ Gov, 16 (2) (2015), pp. 165-178.
|
[102] |
Hemberg E, Rosen J, Warner G, Wijesinghe S, O’Reilly UM. Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood. In: Proceedings of the 15th International Conference on Artificial Intelligence and Law; 2015 Jun 8-12; San Diego, CA, USA. New York City: Association for Computing Machinery (ACM); 2015. p. 79-88.
|
[103] |
E. Hemberg, J. Rosen, G. Warner, S. Wijesinghe, U.M. O’Reilly. Detecting tax evasion: a co-evolutionary approach. Artif Intell Law, 24 (2) (2016), pp. 149-182.
|
[104] |
G. Karafotias, M. Hoogendoorn, Á.E. Eiben. Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput, 19 (2) (2014), pp. 167-187.
|
[105] |
F.G. Lobo, C. Lima, Z. Michalewicz. Parameter setting in evolutionary algorithms. Springer Science & Business Media, Berlin (2007).
|
[106] |
M. Sipper, W. Fu, K. Ahuja, J.H. Moore. Investigating the parameter space of evolutionary algorithms. BioData Min, 11 (1) (2018), 2.
|
[107] |
N. Gilbert, P. Terna. How to build and use agent-based models in social science. Mind Soc, 1 (2000), pp. 57-72.
|
[108] |
E. Samanidou, E. Zschischang, D. Stauffer, T. Lux. Agent-based models of financial markets. Rep Prog Phys, 70 (3) (2007), pp. 409-450.
|
[109] |
N. Gilbert. Agent-based models. SAGE Publications, Newbury Park (2019).
|
[110] |
Antunes L, Balsa J, Coelho H. Agents that collude to evade taxes. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems; 2007 May 14-18; Honolulu, HI, USA. New York City: Association for Computing Machinery (ACM); 2007. p. 1-3.
|
[111] |
F.W.S. Lima. Tax evasion and nonequilibrium model on apollonian networks. Int J Mod Phys C, 23 (11) (2012), 1250079.
|
[112] |
T. Llacer, F.J. Miguel, J.A. Noguera. Tapia E An agent-based model of tax compliance: an application to the Spanish case. Adv Complex Syst, 16 (04n05) (2013), 1350007.
|
[113] |
J.A. Noguera, F.J.M. Quesada, E. Tapia, T. Llàcer. Tax compliance, rational choice, and social influence: an agent-based model. Rev Fr Sociol, 55 (4) (2014), pp. 765-804.
|
[114] |
A.L. Andrei, K. Comer, M. Koehler. An agent-based model of network effects on tax compliance and evasion. J Econ Psychol, 40 (2014), pp. 119-133.
|
[115] |
K.M. Bloomquist. A comparison of agent-based models of income tax evasion. Soc Sci Comput Rev, 24 (4) (2006), pp. 411-425.
|
[116] |
G. Manzo, T. Matthews. Potentialities and limitations of agent-based simulations. Rev Fr Sociol, 55 (4) (2014), pp. 653-688.
|
[117] |
McDonald GW, Osgood ND. Agent-based modeling and its tradeoffs: an introduction & examples. 2023. arXiv:2304.08497.
|
[118] |
Fan W. Graph pattern matching revised for social network analysis. In: Proceedings of the 15th International Conference on Database Theory; 2012 Mar 26-29; Berlin, Germany. New York City: Association for Computing Machinery (ACM); 8-21.
|
[119] |
S. Ma, Y. Cao, W. Fan, J.P. Huai, T. Wo. Strong simulation: capturing topology in graph pattern matching. ACM Trans Database Syst, 39 (1) (2014), pp. 1-46.
|
[120] |
F. Tian, T. Lan, K.M. Chao, N. Godwin, Q. Zheng, N. Shah, et al. Mining suspicious tax evasion groups in big data. IEEE Trans Knowl Data Eng, 28 (10) (2016), pp. 2651-2664.
|
[121] |
Wei W, Yan Z, Ruan J, Zheng Q, Dong B. Mining suspicious tax evasion groups in a corporate governance network. In:Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing; 2017 Aug 21-23; Helsinki, Finland. Berlin:Springer; 2017. p. 465-75.
|
[122] |
Liu L. Methods of detect falsely making out specialized invoices behavior based on directed graph [dissertation]. Xi’an: Xi’an University of Science and Technology; 2017. Chinese.
|
[123] |
J. Ruan, Z. Yan, B. Dong, Q. Zheng, B. Qian. Identifying suspicious groups of affiliated-transaction-based tax evasion in big data. Inf Sci, 477 (2019), pp. 508-532.
|
[124] |
Mathews J, Mehta P, Babu S. Link prediction techniques to handle tax evasion. In: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD); 2021 Jan 2–4; online. New York City: Association for Computing Machinery (ACM); 2021. p. 307–15.
|
[125] |
J.J. Rocha-Salazar, M.J. Segovia-Vargas, M.M. Camacho-Miñano. Detection of shell companies in financial institutions using dynamic social network. Expert Syst Appl, 207 (2022), 117981.
|
[126] |
Chen T, Tsourakakis C. Antibenford subgraphs:unsupervised anomaly detection in financial networks. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; 2022 Aug 14-18; Washington, DC, USA. New York City: Association for Computing Machinery (ACM); 2022. p. 2762-70.
|
[127] |
W. Fan, J. Li, S. Ma, N. Tang, Y. Wu, Y. Wu. Graph pattern matching: from intractable to polynomial time. Proc VLDB Endowment, 3 (1-2) (2010), pp. 264-275.
|
[128] |
Ma S, Cao Y, Huai J, Wu T. Distributed graph pattern matching. In: Proceedings of the 21st International Conference on World Wide Web Conference; 2012 Apr 16-20; Lyon, France. New York City: Association for Computing Machinery (ACM); 949-58.
|
[129] |
S. Bouhenni, S. Yahiaoui, N. Nouali-Taboudjemat, H. Kheddouci. A survey on distributed graph pattern matching in massive graphs. ACM Comput Surv, 54 (2) (2021), pp. 1-35.
|
[130] |
F. Chen, Y.C. Wang, B. Wang, C.C.J. Kuo. Graph representation learning: a survey. APSIPA Trans Signal Inf Process, 9 (1) (2020), e15.
|
[131] |
Khoshraftar S, An A. A survey on graph representation learning methods. 2022. arXiv:2204.01855.
|
[132] |
Matos T, Monteiro JM, Lettich F. An accurate tax fraud classifier with feature selection based on complex network node centrality measure. In:Proceedings of the 19th International Conference on Enterprise Information Systems; 2017 Apr 26-29; Porto, Portugal. Berlin:Springer; 2017. p. 145-51.
|
[133] |
Wu Y, Dong B, Zheng Q, Wei R, Wang Z, Li X. A novel tax evasion detection framework via fused transaction network representation. In: Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC); 2020 Jul 13-17; Madrid, Spain. New York City: IEEE; 235-44.
|
[134] |
Mi L, Dong B, Shi B, Zheng Q. A tax evasion detection method based on positive and unlabeled learning with network embedding features. In:Proceedings of the International Conference on Neural Information Processing; 2020 Nov 18-22; Bangkok, Thailand. Berlin:Springer; 2020. p. 140-51.
|
[135] |
An J, Zheng Q, Wei R, Dong B, Li X. NEUD-TRI:network embedding based on upstream and downstream for transaction risk identification. In: Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC); 2020 Jul 13-17; Madrid, Spain. New York City: IEEE; 277-86.
|
[136] |
Wang Y, Zheng Q, Ruan J, Gao Y, Chen Y, Li X, et al. T-EGAT:a temporal edge enhanced graph attention network for tax evasion detection. In: Proceedings of the 2020 IEEE International Conference on Big Data (Big Data); 2020 Dec 10-13; Atlanta, GA, USA. New York City: IEEE; 2020. p. 1410-5.
|
[137] |
Y. Gao, B. Shi, B. Dong, Y. Wang, L. Mi, Q. Zheng. Tax evasion detection with FBNE-PU algorithm based on PnCGCN and PU learning. IEEE Trans Knowl Data Eng, 35 (1) (2021), pp. 931-944.
|
[138] |
B. Shi, B. Dong, Y. Xu, J. Wang, Y. Wang, Q. Zheng. An edge feature aware heterogeneous graph neural network model to support tax evasion detection. Expert Syst Appl, 213 (2023), 118903.
|
[139] |
Gogoglou A, Bruss CB, Hines KE. On the interpretability and evaluation of graph representation learning. 2019. arXiv:1910.03081.
|
[140] |
R.A. Leite, T. Gschwandtner, S. Miksch, E. Gstrein, J. Kuntner. Visual analytics for event detection: focusing on fraud. Vis Inform, 2 (4) (2018), pp. 198-212.
|
[141] |
J. Yuan, C. Chen, W. Yang, M. Liu, J. Xia, S. Liu. A survey of visual analytics techniques for machine learning. Comput Vis Media, 7 (1) (2021), pp. 3-36.
|
[142] |
D. Liu, S. Alnegheimish, A. Zytek, K. Veeramachaneni. MTV: visual analytics for detecting, investigating, and annotating anomalies in multivariate time series. Proc ACM Hum Comput Interact, 6 (CSCW1) (2022), 103.
|
[143] |
Didimo W, Liotta G, Montecchiani F, Palladino P. An advanced network visualization system for financial crime detection. In: Proceedings of the 2011 IEEE Pacific Visualization Symposium; 2011 Mar 1-4; Hong Kong, China. New York City: IEEE; 203-10.
|
[144] |
Tselykh A, Knyazeva M, Popkova E, Durfee A, Tselykh A. An attributed graph mining approach to detect transfer pricing fraud. In: Proceedings of the 9th International Conference on Security of Information and Networks; 2016 Jul 20-22; Newark, NJ, USA. New York City: Association for Computing Machinery (ACM); 2016. p. 72-5.
|
[145] |
W. Didimo, L. Giamminonni, G. Liotta, F. Montecchiani, D. Pagliuca. A visual analytics system to support tax evasion discovery. Decis Support Syst, 110 (2018), pp. 71-83.
|
[146] |
Zheng Q, Lin Y, He H, Ruan J, Dong B. ATTENet:detecting and explaining suspicious tax evasion groups. In:Proceedings of the 28th International Joint Conference on Artificial Intelligence; 2019 Aug 10-16; Macao, China. Washington, DC: AAAI Press; 2019. p. 6584-6.
|
[147] |
Dai H, Dai B, Song L. Discriminative embeddings of latent variable models for structured data. In: Proceedings of the International Conference on Machine Learning; 2016 Jun 19-24; New York City, NY, USA. New York City: Association for Computing Machinery (ACM); 2016. p. 2702-11.
|
[148] |
Yu H, He H, Zheng Q, Dong B. TaxVis:a visual system for detecting tax evasion group. In: Proceedings of the World Wide Web Conference; 2019 May 13-17; San Francisco, CA, USA. New York City: Association for Computing Machinery (ACM); 2019. p. 3610-4.
|
[149] |
Grover A, Leskovec J. Node2vec:scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 6-11; San Francisco, CA, USA. New York City: Association for Computing Machinery (ACM); 2016. p. 855-64.
|
[150] |
W. Didimo, L. Grilli, G. Liotta, L. Menconi, F. Montecchiani, D. Pagliuca. Combining network visualization and data mining for tax risk assessment. IEEE Access, 8 (2020), pp. 16073-16086.
|
[151] |
Zha Z. TaxAA:a reliable tax auditor assistant for exploring suspicious transactions. In: Proceedings of the Web Conference 2020; 2020 Apr 20-24; Taipei, China. New York City: Association for Computing Machinery (ACM); 240-4.
|
[152] |
Y. Lin, K. Wong, Y. Wang, R. Zhang, B. Dong, H. Qu, et al. TaxThemis: interactive mining and exploration of suspicious tax evasion groups. IEEE Trans Vis Comput Graph, 27 (2) (2021), pp. 849-859.
|
[153] |
Nussbaumer A, Verbert K, Hillemann EC, Bedek MA, Albert D. A framework for cognitive bias detection and feedback in a visual analytics environment. In: Proceedings of the 2016 European Intelligence and Security Informatics Conference (EISIC 2016); 2016 Aug 16-19; Uppsala, Sweden. New York City: IEEE; 148-51.
|
[154] |
Wall E, Blaha LM, Franklin L, Endert A. Warning, bias may occur:a proposed approach to detecting cognitive bias in interactive visual analytics. In: Proceedings of the 2017 IEEE Conference on Visual Analytics Science and Technology (VAST 2017); 2017 Oct 3-6; Phoenix, AZ, USA. New York City: IEEE. 2017. p. 104-15.
|
[155] |
E. Wall. Detecting and mitigating human bias in visual analytics [dissertation]. Georgia Institute of Technology, Atlanta (2020).
|
[156] |
Q. Zheng. 2019 big data knowledge engineering and application. J Comput Res Dev, 56 (12) (2019), pp. 2519-2520.
|
[157] |
F. Wu, Y. Han, X. Li, Q.H. Zheng. Chen XL Reasoning in artificial intelligence: advances and challenges. Bull Natl Nat Sci Found Chin, 32 (3) (2018), pp. 262-265.Chinese.
|
[158] |
Y. Zhuang, F. Wu, C. Chen, Y. Pan. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inf Technol Electron Eng, 18 (1) (2017), pp. 3-14.
|
[159] |
Y. Yang, Y. Zhuang, Y. Pan. Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies. Front Inf Technol Electron Eng, 22 (12) (2021), pp. 1551-1558.
|
[160] |
Q. Zheng, J. Liu, H. Zeng, Z. Guo, B. Wu, B. Wei. Knowledge forest: a novel model to organize knowledge fragments. Sci China Inf Sci, 64 (7) (2021), 179103.
|