Open Access
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
Article Number 02042
Number of page(s) 4
Section Machine Learning and Energy Industry Structure Forecast Analysis
Published online 07 December 2020
  1. Dhingra S. (2019). Comparative Analysis of algorithms for Credit Card Fraud Detection using Data Mining: A Review. Journal of Advanced Database Management & Systems, 6(2), 12-17. [Google Scholar]
  2. Minastireanu, E.A., & Mesnita G. (2019). Light gbm machine learning algorithm to online click fraud detection. J. Inform. Assur. Cyberse-cur, 2019. [Google Scholar]
  3. Bhusari V., & Patil S. (2016). Study of hidden markov model in credit card fraudulent detection. In 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave) (pp. 1-4). IEEE. [Google Scholar]
  4. Fang Y., Zhang Y., & Huang C. Credit Card Fraud Detection Based on Machine Learning. Fu K., Cheng D., Tu Y., & Zhang L. (2016, October). Credit card fraud detection using convolutional neural networks. In International Conference on Neural Information Processing (pp. 483-490). Springer, Cham. [Google Scholar]
  5. Bahnsen, A.C., Aouada D., Stojanovic A., & Ottersten B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142. [CrossRef] [Google Scholar]
  6. Tolles J., & Meurer, W.J. (2016). Logistic regression: relating patient characteristics to outcomes. Jama, 316(5), 533-534. [Google Scholar]
  7. Murphy, K.P. (2006). Naive bayes classifiers. University of British Columbia, 18, 60. [Google Scholar]
  8. [Google Scholar]
  9. Chen T., & Guestrin C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). [Google Scholar]
  10. Friedman, J.H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38(4), 367-378. [CrossRef] [Google Scholar]
  11. Suykens, J.A., & Vandewalle J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.