1,415
Views
38
CrossRef citations to date
0
Altmetric
Articles

Predicting online gambling self-exclusion: an analysis of the performance of supervised machine learning models

, , &
Pages 193-210 | Received 02 Jul 2015, Accepted 31 Jan 2016, Published online: 10 Apr 2016

References

  • Agresti, A. (2007). An introduction to categorical data analysis (2nd ed). Hoboken, NJ: Wiley-Interscience.10.1002/0470114754
  • Blaszczynski, A. , & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction , 97 , 487–499.10.1046/j.1360-0443.2002.00015.x
  • Bowyer, K. , Chawla, N. , Hall, L. , & Kegelmeyer, P. (2002). SMOTE: Synthetic minority over sampling technique. Journal Of Artificial Intelligence Research , 16 , 321–357.
  • Braverman, J. , & Shaffer, H. J. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk Internet gambling. European Journal of Public Health , 22, 273–278. doi:10.1093/eurpub/ckp232.
  • Breiman, L. (2001, October). Random forests. Machine Learning , 45 , 5–32.10.1023/A:1010933404324
  • Cooper, G. , & Herskovits, E. (1991). A Bayesian method for constructing Bayesian belief networks from databases. In D'Ambrosio , Smets , & Bonissone (Eds.), Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence ( UAI'91) (pp. 86–94). San Francisco, CA, USA: Morgan Kaufmann Inc.
  • Cummins, L.F. , Nadorff, M.R. , & Kelly, A.E. (2009). Winning and positive affect can lead to reckless gambling. Psychology of Addictive Behaviors , 23 , 287–294.10.1037/a0014783
  • D'Avila Garcez, A.S. , Broda, K. , & Gabbay, D. (2002). Neural-symbolic learning systems: Foundations and applications, perspectives in neural computing . London: Springer.10.1007/978-1-4471-0211-3
  • D'Avila Garcez, A.S. , Lamb, L.C. , & Gabbay, D.M. (2006). Connectionist computations of intuitionistic reasoning. Theoretical Computer Science Journal , 358 , 34–55.10.1016/j.tcs.2005.11.043
  • D'Avila Garcez, A. , Gabbay, D.M. , Ray, O. , & Woods, J. (2007). Abductive reasoning in neural-symbolic learning systems. Topoi: An International Review of Philosophy , 26 , 37–49.10.1007/s11245-006-9005-5
  • D'Avila Garcez, A. S. , Lamb, L. C. , & Gabbay, D. M. (2009). Neural-symbolic cognitive reasoning. cognitive technologies . Berlin: Springer.
  • Dragičević, S. , Percy, C. , Kudic, A. , & Parke, J. (2013). A descriptive analysis of demographic and behavioral data from internet gamblers and those who self-exclude from online gambling platforms. Journal of Gambling Studies. Advance online publication. doi:10.1007/s10899-013-9418-1.
  • Dragičević, S. , Tsogas, G. , & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies , 11 , 377–391.10.1080/14459795.2011.629204
  • Ferris & Wynne . (2001). The Canadian problem gambling index: Final report . Ottawa, ON: Canadian Centre on Substance Abuse.
  • França, M. , Zaverucha, G. , & D’Avila Garcez, A. (2014). Fast relational learning using bottom clause propositionalization with artificial neural networks. Machine Learning , 94 , 81–104.10.1007/s10994-013-5392-1
  • Freedman, D. (2009). Statistical models: Theory and practice . Cambridge: Cambridge University Press.10.1017/CBO9780511815867
  • Gainsbury, S. (2011). Player account-based gambling: Potentials for behaviour-based research methodologies. International Gambling Studies , 11 , 153–171.10.1080/14459795.2011.571217
  • Geisser, S. (1993). Predictive inference . New York, NY : Chapman and Hall.10.1007/978-1-4899-4467-2
  • Ha, T. , & Bunke, H. (1997). Off-line, handwritten numeral recognition by perturbation method. Pattern Analysis and Machine Intelligence , 19 , 535–539.10.1109/34.589216
  • Hall, M. , Frank, E. , Holmes, G. , Pfahringer, B. , Reutemann, P. , & Witten, I. (2009). The WEKA data mining software: An update . SIGKDD Explorations, 11 , Issue 1. WEKA.
  • Hayer, T. , & Meyer, G. (2011a). Self-exclusion as a harm minimization strategy: Evidence for the casino sector from selected European countries. Journal of Gambling Studies , 27 , 685–700.10.1007/s10899-010-9227-8
  • Hayer, T. , & Meyer, G. (2011b). Internet self-exclusion: Characteristics of self-excluded gamblers and preliminary evidence for its effectiveness. International Journal of Mental Health and Addiction , 9 , 307–596.
  • Hosmer, D. , & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley.10.1002/0471722146
  • Johansson, A. , Grant, J. E. , Kim, S. W. , Odlaug, B. L. , & Gotestam, G. K. (2009). Risk factors for problematic gambling: A critical literature review. Journal of Gambling Studies , 25 , 67–92.
  • Mitchell, T. (1997). Machine learning (1st ed.). New York, NY : McGraw-Hill.
  • Nakamura, M. , Kajiwara, Y. , Otsuka, A. , & Kimura, H . (2013). LVQ-SMOTE - learning vector quantization based synthetic minority over–sampling technique for biomedical data. BioData Mining , 6 , 16. 10.1186/1756-0381-6-16
  • La Brie, R. , & Shaffer, H. (2011). Identifying behavioral markers of disordered Internet sports gambling. Addiction Research and Theory , 19 , 56–65.10.3109/16066359.2010.512106
  • LaPlante, D. A. , Nelson, S. E. , & Gray, H. M. (2013, August 5). Breadth and depth involvement: Understanding internet gambling involvement and its relationship to gambling problems. Psychology of Addictive Behaviors . Advance online publication. doi: 10.1037/a0033810
  • Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference . San Francisco, CA, USA: Morgan Kaufmann Inc..
  • Pearl, J. (2000). Causality: Models, reasoning, and inference . Cambridge: Cambridge University Press.
  • Philander, K. (2014). Identifying high risk online gamblers: A comparison of data mining procedures. International Gambling Studies , 14 , 53–63. doi:10.1080/14459795.2013.841721.
  • Quinlan, J. (1986, March). Induction of decision trees. Mach. Learn , 1 , 81–106.
  • Rumelhart, D. , Hinton, G. , & Williams, R . (1985). Learning internal representations by error propagation . No. ICS-8506. California University of San Diego La Jolla, Institute for Cognitive Science.
  • Russell, S. , & Norvig, P. (2003). Artificial intelligence: A modern approach (2 ed.). Upper Saddle River, NJ : Prentice Hall/Pearson Education.
  • Schellinck, T. , & Schrans, T. (2011). Intelligent design: How to model gambler risk assessment by using loyalty tracking data. Journal of Gambling Issues , 26 , 51–68. doi:10.4309/jgi.2011.26.5.
  • Stokes, M. E. , Davis, C. S. , & Koch, G. G. (1995). Categorical data analysis using the SAS system . Cary, NC: SAS Institute.
  • Turner, E.N. (2008). Games, gambling and gambling problems. In Zangeneh , Blaszczynski , & Turner (Eds.), The pursuit of winning: Problem gambling theory, research and treatment (pp. 33–64). Berlin, Heidelberg, New York: Springer.10.1007/978-0-387-72173-6
  • Wardle, H. (2012). Understanding self-exclusion – Profile, processes and improvements: Evidence and implications from a research study of online betting exchange users . Paper presented at meeting of Responsible Gambling Council Discovery 2012 Conference.
  • Williams, R.J. , Volberg, R.A. & Stevens, R.M.G. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends . Report prepared for the Ontario Problem Gambling Research Centre and the Ontario Ministry of Health and Long Term Care. May 8, 2012.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.