An Empirical Study of Learning from Imbalanced Data Using Random Forest

By Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse. IEEE International Conference on Tools with Artificial Intelligence, 2007

352 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An up-to-date comparison of state-of-the-art classification algorithms (2017, methodology): “In our experiments, we ﬁx NTrees to be 100 ( Khoshgoftaar et al., 2007 ) and vary NVarToSample from 1 to the total number of features, to ﬁnd out the value for NVarToSample that can achieve the best classiﬁ-cation accuracy on v alid ation _ d ata .”
Training and assessing classification rules with imbalanced data (2012, background): “Some references are Liu et al. (2006); Thomas et al. (2006); Sun et al. (2007); Khoshgoftaar et al. (2007, 2011); Fernandez et al. (2012).”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “…methods: (i) low training time complexity, O (nloд(n)) (Witten et al. 2016), and fast prediction; (ii) resilience to deal with imbalanced datasets (Khoshgoftaar et al. 2007); (iii) embedded feature selection method and intrinsic metrics to rank features by importance; and (iv) able to deal…”
A survey on addressing high-class imbalance in big data (2018, methodology): “In the case of Ensemble Methods designed to address the class imbalance problem, generally Bagging, AdaBoost, and Random Forest are popular approaches [42, 43].”
Data mining for credit card fraud: A comparative study (2011, methodology): “Following [6] and as found to be a generally good setting for b in [27], we set b=√B.”