Effect of data resampling on feature importance in imbalanced blockchain data: comparison studies of resampling techniques

This publication has 20 references indexed in Scilit:

• Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection
  Computational Economics, 2018
• Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning
  2018
• An insight into imbalanced Big Data classification: outcomes and challenges
  Complex & Intelligent Systems, 2017
• Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection
  Applied Soft Computing, 2014
• Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification
  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010
• Learning from Imbalanced Data
  IEEE Transactions on Knowledge and Data Engineering, 2009
• Extremely randomized trees
  Machine Learning, 2006
• Random Forests
  Machine Learning, 2001
• Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
  Atmospheric Environment, 1998
• Individual Comparisons by Ranking Methods
  Biometrics Bulletin, 1945