Mining data with random forests: A survey and results of new tests

By Antanas Verikas, Adas Gelzinis, Marija Bacauskiene. 2011

721 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “VIM calculates the correlation between high-dimensional datasets on the basis of internal proximity matrix measurements [122] or identifying outliers in the training samples by exploratory examination of sample proximities through the use of variable importance metrics [44].”
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, methodology): “The prediction performance of RF compares well to other classification algorithms [7] such as support vector machines (SVMs, [9, 10]), artificial neural networks [11–13], Bayesian classifiers [14, 15], logistic regression [16], k-nearest-neighbours [17], discriminant analysis such as Fisher’s linear”
A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, methodology): “In the experiment, we use the same settings for the RF models as recommended in [65] and [66], which are presented in Table I.”
On the use of MapReduce for imbalanced big data using Random Forest (2014, background): “RF popularity comes from its good performance in relation with other classification algorithms [53].”
An experimental study of the intrinsic stability of random forest variable importance measures (2016, methodology): “Verikas et al explored the MDA stability by observing the Spearman coefficient of feature rankings obtained in 20 different runs [24].”
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012, background): “…the RF algorithm and its applications to genetic epidemiology, Chen et al. [18] give an extensive overview of applications of recursive partitioning to bioinformatics, and Verikas et al. [76] survey RF applications and their performance in comparison with other methods in a more general context.”
Random Forests for Big Data (2015, background): “Key words: Random Forest, Big Data, Parallel Computing, Bag of Little Bootstraps, On-line Learning”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, background): “We note that Verikas et al. (2011) provide a survey for Random Forest applications in many fields, which includes only one reference of application on IDSs.”
VSURF: An R Package for Variable Selection Using Random Forests (2015, positive): “The RF algorithm is a very popular machine learning algorithm and appears to be powerful in a lot of different applications, see for example Verikas et al. (2011) and Boulesteix et al. (2012) for recent surveys.”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “[70]) is doable with random forests, through the use of variable importance metrics.”
Explaining machine learning models in sales predictions (2017, methodology): “To build a classifier, we use the ensemble learning method Random Forest (RF) (Breiman, 2001), broadly recognized as a robust and well-performing ML method (Verikas et al., 2011).”